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Communications and Networking Research Group

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134. Vishrant Tripathi, Nick Jones, Eytan Modiano, Fresh-CSMA: A Distributed Protocol for Minimizing Age of Information, IEEE Journal on Communications and Networks, 2024.

133. Bai Liu, Quang Nguyen, Qingkai Liang, Eytan Modiano, Tracking Drift-Plus-Penalty: Utility Maximization for Partially Observable and Controllable Networks, IEEE/ACM Transactions on Networking, 2024.

132. Xinzhe Fu, Eytan Modiano, Optimal Routing to Parallel Servers with Unknown Utilities – Multi-armed Bandit With Queues, IEEE/ACM Transactions on Networking, January 2022.

131. Bai Liu, Qingkai Liang, Eytan Modiano, Tracking MaxWeight: Optimal Control for Partially Observable and Controllable Networks, IEEE/ACM Transactions on Networking, August 2023.

130. Xinzhe Fu, Eytan Modiano, Joint Learning and Control in Stochastic Queueing Networks with unknown Utilities, Proceedings of the ACM on Measurement and Analysis of Computing Systems, 2023.

129. Vishrant Tripathi, Rajat Talak, Eytan Modiano, Information Freshness in Multi-Hop Wireless Networks, IEEE/ACM Transactions on Networking,” April 2023.

128.  Xinzhe Fu, Eytan Modiano, “ Learning-NUM: Network Utility Maximization with Unknown Utility Functions and Queueing Delay ,”  IEEE/ACM Transactions on Networking,” 2022.

127.  Bai Liu, Qiaomin Xie, Eytan Modiano,  “ RL-QN: A Reinforcement Learning Framework for Optimal Control of Queueing Systems ,”  ACM Transactions on Modeling and Performance Evaluation of Computing Systems (TOMPECS), 2022.

126. Xinzhe Fu and E. Modiano,  “ Elastic Job Scheduling with Unknown Utility Functions ,” Performance Evaluation, 2021.

125. Bai Liu and E. Modiano, “ Optimal Control for Networks with Unobservable Malicious Nodes ,”  Performance Evaluation, 2021.

124. Vishrant Tripathi, Rajat Talak, Eytan Modiano, “ Age Optimal Information Gathering and Dissemination on Graphs ,”  Transactions on Mobile Computing, April 2021.

123.  Xinyu Wu, Dan Wu, Eytan Modiano, “ Predicting Failure Cascades in Large Scale Power Systems via the Influence Model Framework, ”  IEEE Transactions on Power Systems, 2021.

122.   Roy D. Yates, Yin Sun, D. Richard Brown III, Sanjit K. Kaul, Eytan Modiano and Sennur Ulukus, “ Age of Information: An Introduction and Survey, ”  Journal on Selected Areas in Communications, February 2021.

121.   Jianan Zhang, Abhishek Sinha, Jaime Llorca, Anonia Tulino, Eytan Modiano, “ Optimal Control of Distributed Computing Networks with Mixed-Cast Traffic Flows ,”  IEEE/ACM Transactions on Networking, 2021.

120.   Thomas Stahlbuhk, Brooke Shrader, Eytan Modiano, “ Learning Algorithms for Minimizing Queue Length Regret ,”  IEEE Transactions on Information Theory, 2021.

119.   Thomas Stahlbuhk, Brooke Shrader, Eytan Modiano, “ Throughput Maximization in Uncooperative Spectrum Sharing Networks ,”  IEEE/ACM IEEE/ACM Transactions on Networking, Vol. 28, No. 6, December 2020.

118.   Thomas Stahlbuhk, Brooke Shrader, Eytan Modiano, “ Learning algorithms for scheduling in wireless networks with unknown channel statistics ,” Ad Hoc Networks, Vol. 85, pp. 131-144, 2019.

117.   Rajat Talak, Eytan Modiano, “ Age-Delay Tradeoffs in Queueing Systems ,”  IEEE Transactions on Information Theory, 2021.

116.   Rajat Talak, Sertac Karaman, Eytan Modiano, “ Improving Age of Information in Wireless Networks with Perfect Channel State Information ,”  IEEE/ACM Transactions on Networking, Vol. 28, No. 4, August 2020.

115.   Igor Kadota and Eytan Modiano, “ Minimizing the Age of Information in Wireless Networks with Stochastic Arrivals ,” IEEE Transactions on Mobile Computing, 2020.

114.   Rajat Talak, Sertac Karaman, Eytan Modiano, “ Optimizing Information Freshness in Wireless Networks under General Interference Constraints ,”  IEEE/ACM transactions on Networking, Vol. 28, No. 1, February 2020.

113.   X. Fu and E. Modiano, “ Fundamental Limits of Volume-based Network DoS Attacks ,” Proceedings of the ACM on Measurement and Analysis of Computing Systems, Vol. 3, No. 3, December 2019. 

112.   Rajat Talak, Sertac Karaman, Eytan Modiano, “ Capacity and Delay Scaling for Broadcast Transmission in Highly Mobile Wireless Networks ,” IEEE Transactions on Mobile Computing, 2019.

111.   Abhishek Sinha and Eytan Modiano, “ Throughput-Optimal Broadcast in Wireless Networks with Point-to-Multipoint Transmissions , IEEE Transactions on Mobile Computing, Vol. 19, No. 9, September 2020.

110.   Yu-Pin Hsu, Eytan Modiano, Lingjie Duan, “ Scheduling Algorithms for Minimizing Age of Information in Wireless Broadcast Networks with Random Arrivals ,”  IEEE Transactions on Mobile Computing, Vol. 19, No. 12, December 2020.

109.   Xiaolin Jiang, Hossein S. Ghadikolaei, Gabor Fodor, Eytan Modiano, Zhibo Pang, Michele Zorzi, Carlo Fischione, “ Low-latency Networking: Where Latency Lurks and How to Tame It ,”  Proceedings of the IEEE, 2019.

108.   Jianan Zhang, Edmund Yeh, Eytan Modiano, “ Robustness of Interdependent Random Geometric Networks ,” IEEE Transactions on Network Science and Engineering, Vol. 6, No. 3, July-September 2019.

107.   Qingkai Liang, Hyang-Won Lee, Eytan Modiano, “ Robust Design of Spectrum-Sharing Networks ,” IEEE Transactions on Mobile Computing, Vol. 18, No. 8, August 2019.

106.   A. Sinha, L. Tassiulas, E. Modiano, “ Throughput-Optimal Broadcast in Wireless Networks with Dynamic Topology ,”  IEEE Transactions on Mobile Computing, Vol. 18, No. 5, May 2019.

105. Igor Kadota, Abhishek Sinha, Eytan Modiano, “ Scheduling Algorithms for Optimizing Age of Information in Wireless Networks With Throughput Constraints ,”  IEEE/ACM Transactions on Networking, August 2019.

104.   Igor Kadota, Abhishek Sinha, Rahul Singh, Elif Uysal-Biyikoglu, Eytan Modjano, “ Scheduling Policies for Minimizing Age of Information in Broadcast Wireless Networks ,” IEEE/ACM Transactions on Networking, Vol. 26, No. 5, October 2018.

103.   Jianan Zhang and Eytan Modiano, “ Connectivity in Interdependent Networks ,”  IEEE/ACM Transactions on Networking, 2018.

102.   Qingkai Liang, Eytan Modiano, “ Minimizing Queue Length Regret Under Adversarial Network Models ,” Proceedings of the ACM on Measurement and Analysis of Computing Systems, Volume 2, Issue 1, April 2018, Article No.: 11, pp 1-32. (same as Sigmetrics 2018).

101.   A. Sinha and E. Modiano, “ Optimal Control for Generalized Network Flow Problems ,”  IEEE/ACM Transactions on Networking, 2018.

100.   Hossein Shokri-Ghadikolaei, Carlo Fischione, Eytan Modiano  “ Interference Model Similarity Index and Its Applications to mmWave Networks ,”  IEEE Transactions on Wireless Communications, 2018.

99.   Matt Johnston, Eytan Modiano, “ Wireless Scheduling with Delayed CSI: When Distributed Outperforms Centralized, ’ IEEE Transactions on Mobile Computing, 2018.

98.   A. Sinha, G. Paschos, E. Modiano, “ Throughput-Optimal Multi-hop Broadcast Algorithms ,” IEEE/ACM Transactions on Networking, 2017.

97.   Nathan Jones, Georgios Paschos, Brooke Shrader, Eytan Modiano, “ An Overlay Architecture for Throughput Optimal Multipath Routing ,” IEEE/ACM Transactions on Networking, 2017.

96.   Greg Kuperman, Eytan Modiano, “ Providing Guaranteed Protection in Multi-Hop Wireless Networks with Interference Constraints ,” IEEE Transactions on Mobile Computing, 2017.

95.   Matt Johnston, Eytan Modiano, Isaac Kesslassy, “ Channel Probing in Opportunistic Communications Systems ,”  IEEE Transactions on Information Theory, November, 2017.

94.   Anurag Rai, Georgios Paschos, Chih-Ping Lee, Eytan Modiano, “ Loop-Free Backpressure Routing Using Link-Reversal Algorithms “, IEEE/ACM Transactions on Networking, October, 2017.

93.   Matt Johnston and Eytan Modiano, “” Controller Placement in Wireless Networks with Delayed CSI ,” IEEE/ACM Transactions on Networking, 2017.

92.   Jianan Zheng, E. Modiano, D. Hay, “ Enhancing Network Robustness via Shielding ,”  IEEE Transactions on Networking, 2017.

91.   M. Markakis, E. Modiano, J.N. Tsitsiklis, “ Delay Analysis of the Max-Weight Policy under Heavy-Tailed Traffic via Fluid Approximations ,” Mathematics of Operations Research, October, 2017.

90.   Qingkai Liang and E. Modiano, “ Survivability in Time-Varying Graphs ,”  IEEE Transactions on Mobile Computing, 2017.

89.   A. Sinha, G. Paschos, C. P. Li, and E. Modiano, “ Throughput-Optimal Multihop Broadcast on Directed Acyclic Wireless Networks ,” IEEE/ACM Transactions on Networking, Vol. 25, No. 1, Feb. 2017.

88.   G. Celik, S. Borst, , P. Whiting , E. Modiano, “ Dynamic Scheduling with Reconfiguration Delays ,”  Queueing Systems, 2016.

87.  G. Paschos, C. P. Li, E. Modiano, K. Choumas, T. Korakis, “ In-network Congestion Control for Multirate Multicast ,”   IEEE/ACM Transactions on Networking,  2016.

86.   H. Seferoglu and E. Modiano, “ TCP-Aware Backpressure Routing and Scheduling ,” IEEE Transactions on Mobile Computing, 2016.

85.   H. Seferoglu and E. Modiano, “ Separation of Routing and Scheduling in Backpressure-Based Wireless Networks ,” IEEE/ACM Transactions on Networking, Vol. 24, No. 3, 2016.

84.   M. Markakis, E. Modiano, J.N. Tsitsiklis, “ Delay Stability of Back-Pressure Policies in the presence of Heavy-Tailed Traffic ,”  IEEE/ACM Transactions on Networking, 2015.

83.   S. Neumayer, E. Modiano,  “ Network Reliability Under Geographically Correlated Line and Disk Failure Models ,” Computer Networks, to appear, 2016.

82.   S. Neumayer, E. Modiano, A. Efrat, “ Geographic Max-Flow and Min-Cut Under a Circular Disk Failure Model ,” Computer Networks, 2015.

81.   Marzieh Parandehgheibi, Hyang-Won Lee, Eytan Modiano, Survivable Path Sets:  A new approach to survivability in multi-layer networks ,”  IEEE Journal on Lightwave Technology, 2015.

80.   G. Kuperman, E. Modiano, A. Narula-Tam, “ Network Protection with Multiple Availability Guarantees ,” Computer Networks, 2015.

79.   G. Kuperman, E. Modiano, A. Narula-Tam, “ Analysis and Algorithms for Partial Protection in Mesh Networks ,” IEEE/OSA Journal of Optical Communications and Networks, 2014.

78.   Krishna Jagannathan, Mihalis Markakis, Eytan Modiano, John Tsitsiklis, “ Throughput Optimal Scheduling over Time-Varying Channels in the presence of Heavy-Tailed Traffic ,” IEEE Transactions on Information Theory, 2014.

77.   Chih-Ping Li and Eytan Modiano, “ Receiver-Based Flow Control for Networks in Overload ,” IEEE/ACM Transactions on Networking, Vol. 23, No. 2, 2015.

76.   Matthew Johnston, Hyang-Won Lee, Eytan Modiano, “ A Robust Optimization Approach to Backup Network Design with Random Failures ,” IEEE/ACM Transactions on Networking, Vol. 23, No. 4, 2015.

75.   Guner Celik and Eytan Modiano, “ Scheduling in Networks with Time-Varying Channels and Reconfiguration Delay ,” IEEE/ACM Transactions on Networking, Vol. 23, No. 1, 2015.

74.   Matt Johnston, H.W. Lee, E. Modiano, “ Robust Network Design for Stochastic Traffic Demands ,” IEEE Journal of Lightwave Technology, 2013.

73.   Mihalis Markakis, Eytan Modiano, John Tsitsiklis, “ Max-Weight Scheduling in Queueing Networks With Heavy-Tailed Traffic, ” IEEE/ACM Transactions on Networking, 2014.

72.   Kayi Lee, Hyang-Won Lee, Eytan Modiano, “ Maximizing Reliability in WDM Networks through Lightpath Routing ,”  IEEE ACM Transactions on Networking, 2014.

71.   Krishna Jaggannathan and Eytan Modiano, “ The Impact of Queue Length Information on Buffer Overflow in Parallel Queues ,”  IEEE transactions on Information Theory, 2013.

70.   Krishna Jagannathan, Ishai Menashe, Gil Zussman, Eytan Modiano, “ Non-cooperative Spectrum Access – The Dedicated vs. Free Spectrum Choice ,” IEEE JSAC, special issue on Economics of Communication Networks & Systems, to appear, 2012.

69.   Guner Celik and Eytan Modiano, “ Dynamic Server Allocation over Time Varying Channels with Switchover Delay ,” IEEE Transactions on Information Theory, to appear, 2012.

68.   Anand Srinivas and Eytan Modiano, “ Joint Node Placement and Assignment for Throughput Optimization in Mobile Backbone Networks ,” IEEE JSAC, special issue on Communications Challenges and Dynamics for Unmanned Autonomous Vehicles, June, 2012.

67.   Guner Celik and Eytan Modiano, “ Controlled Mobility in Stochastic and Dynamic Wireless Networks ,” Queueing Systems, 2012.

66.   Krishna Jagannathan, Shie Mannor, Ishai Menache, Eytan Modiano, “ A State Action Frequency Approach to Throughput Maximization over Uncertain Wireless Channels ,” Internet Mathematics, Vol. 9, Nos. 2–3: 136–160.

65.   Long Le, E. Modiano, N. Shroff, “Optimal Control of Wireless Networks with Finite Buffers ,” IEEE/ACM Transactions on Networking, to appear, 2012.

64.   K. Jagannathan, M. Markakis, E. Modiano, J. Tsitsiklis, “Queue Length Asymptotics for Generalized Max-Weight Scheduling in the presence of Heavy-Tailed Traffic,” IEEE/ACM Transactions on Networking, Vol. 20, No. 4, August 2012.

63.   Kayi Lee, Hyang-Won Lee, Eytan Modiano, “ Reliability in Layered Networks with Random Link Failures, ” IEEE/ACM Transactions on Networking, December 2011.

62.   Krishna Jagannathan, Eytan Modiano, Lizhong Zheng, “ On the Role of Queue Length Information in Network Control ,” IEEE Transactions on Information Theory, September 2011.

61.   Hyang-Won Lee, Long Le, Eytan Modiano, “ Distributed Throughput Maximization in Wireless Networks via Random Power Allocation, ” IEEE Transactions on Mobile Computing, 2011.

60.   Sebastian Neumayer, Gil Zussman, Rueven Cohen, Eytan Modiano, “ Assessing the Vulnerability of the Fiber Infrastructure to Disasters, ” IEEE/ACM Transactions on Networking, December 2011.

59.   Kayi Lee, Eytan Modiano, Hyang-Won Lee, “ Cross Layer Survivability in WDM-based Networks ,” IEEE/ACM Transactions on Networking, August 2011.

58.   Emily Craparo, Jon How, and Eytan Modiano, “Throughput Optimization in Mobile Backbone Networks,” IEEE Transactions on Mobile Computing, April, 2011.

57.   Hyang-Won Lee, Kayi Lee, and Eytan Modiano, “Diverse Routing in Networks with Probabilistic Failures,” IEEE/ACM Transactions on Networking, December, 2010.

56.   Guner Celik, Gil Zussman, Wajahat Khan and Eytan Modiano, “MAC Protocols For Wireless Networks With Multi-packet Reception Cabaility ,” IEEE Transactions on Mobile Computing, February, 2010.

55.   Atilla Eryilmaz, Asuman Ozdaglar, Devavrat Shah, and Eytan Modiano, “Distributed Cross-Layer Algorithms for the Optimal Control of Multi-hop Wireless Networks,” IEEE/ACM Transactions on Networking, April 2010.

54.   Murtaza Zafer and Eytan Modiano, “Minimum Energy Transmission over a Wireless Channel With Deadline and Power Constraints ,” IEEE Transactions on Automatic Control, pp. 2841-2852, December, 2009.

53.   Murtaza Zafer and Eytan Modiano, “A Calculus Approach to Energy-Efficient Data Transmission with Quality of Service Constraints,” IEEE/ACM Transactions on Networking, 2009.

52.   Anand Srinivas, Gil Zussman, and Eytan Modiano, “Construction and Maintenance of Wireless Mobile Backbone Networks,” IEEE/ACM Transactions on Networking, 2009.

51.   Andrew Brzezinski, Gil Zussman, and Eytan Modiano, “Distributed Throughput Maximization in Wireless Mesh Networks Via Pre-Partitioning,” IEEE/ACM Transactions on Networking, December, 2008.

50.   Amir Khandani, Eytan Modiano, Jinane Abounadi, Lizhong Zheng, “Reliability and Route Diversity in Wireless Networks,” IEEE Transactions on Wireless Communications, December, 2008.

49.   Alessandro Tarello, Jun Sun, Murtaza Zafer and Eytan Modiano, “Minimum Energy Transmission Scheduling Subject to Deadline Constraints,” ACM Wireless Networks, October, 2008.

48.   Murtaza Zafer, Eytan Modiano, “Optimal Rate Control for Delay-Constrained Data Transmission over a Wireless Channel,” IEEE Transactions on Information Theory, September, 2008.

47.   Andrew Brzezinski and Eytan Modiano, “Achieving 100% Throughput In Reconfigurable IP/WDM Networks,” IEEE/ACM Transactions on Networking, August, 2008.

46.   Michael Neely, Eytan Modiano and C. Li, “Fairness and Optimal Stochastic Control for Heterogeneous Networks,” IEEE/ACM Transactions on Networking, September, 2008.

45.   Amir Khandani, Jinane Abounadi, Eytan Modiano, Lizhong Zheng, “Cooperative Routing in Static Wireless Networks,” IEEE Transactions on Communications, November 2007.

44.   Murtaza Zafer, Eytan Modiano, “Joint Scheduling of Rate-guaranteed and Best-effort Users over a Wireless Fading Channel,” IEEE Transactions on Wireless Communications, October, 2007.

43.   Krishna Jagannathan, Sem Borst, Phil Whiting and Eytan Modiano, “Scheduling of Multi-Antenna Broadcast Systems with Heterogeneous Users,” IEEE Journal of Selected Areas in Communications, September, 2007.Amir Khandani, Jinane

42.   Anand Ganti, Eytan Modiano, and John Tsitsiklis, “Optimal Transmission Scheduling in Symmetric Communication Models with Intermittent Connectivity, ” IEEE Transactions on Information Theory, March, 2007.

41.   Michael Neely and Eytan Modiano, “Logarithmic Delay for NxN Packet Switches Under Crossbar Constraints,” IEEE/ACM Transactions on Networking, November, 2007.

40.   Jun Sun, Jay Gao, Shervin Shambayati and Eytan Modiano, “Ka-Band Link Optimization with Rate Adaptation for Mars and Lunar Communications,”   International Journal of Satellite Communications and Networks, March, 2007.

39.   Jun Sun and Eytan Modiano, “Fair Allocation of A Wireless Fading Channel: An Auction Approach” Institute for Mathematics and its Applications, Volume 143: Wireless Communications, 2006.

38.   Jun Sun, Eytan Modiano and Lizhong Zhang, “Wireless Channel Allocation Using An Auction Algorithm,” IEEE Journal on Selected Areas in Communications, May, 2006.

37.   Murtaza Zafer and Eytan Modiano, “Blocking Probability and Channel Assignment for Connection Oriented Traffic in Wireless Networks,” IEEE Transactions on Wireless Communications, April, 2006.

36.   Alvin Fu, Eytan Modiano, and John Tsitsiklis, “Optimal Transmission Scheduling over a fading channel with Energy and Deadline Constraints” IEEE Transactions on Wireless Communications, March,2006.

35.   Poompat Saengudomlert, Eytan Modiano and Rober Gallager, “On-line Routing and Wavelength Assignment for Dynamic Traffic in WDM Ring and Torus Networks,” IEEE Transactions on Networking, April, 2006.

34.   Li-Wei Chen, Eytan Modiano and Poompat Saengudomlert, “Uniform vs. Non-Uniform band Switching in WDM Networks,” Computer Networks (special issue on optical networks), January, 2006.

33.   Andrew Brzezinski and Eytan Modiano, “Dynamic Reconfiguration and Routing Algorithms for IP-over-WDM networks with Stochastic Traffic,” IEEE Journal of Lightwave Technology, November, 2005

32.   Randall Berry and Eytan Modiano, “Optimal Transceiver Scheduling in WDM/TDM Networks,” IEEE Journal on Selected Areas in Communications, August, 2005.

31.   Poompat Saengudomlert, Eytan Modiano, and Robert G. Gallager, “Dynamic Wavelength Assignment for WDM All-Optical Tree Networks,” IEEE Transactions on Networking, August, 2005.

30.   Ashwinder Ahluwalia and Eytan Modiano, “On the Complexity and Distributed Construction of Energy Efficient Broadcast Trees in Wireless Ad Hoc Networks,” IEEE Transactions on Wireless Communications, October, 2005.

29.   Michael Neely, Charlie Rohrs and Eytan Modiano, “Equivalent Models for Analysis of Deterministic Service Time Tree Networks,” IEEE Transactions on Information Theory, October, 2005.

28.   Michael Neely and Eytan Modiano, “Capacity and Delay Tradeoffs for Ad Hoc Mobile Networks,” IEEE Transactions on Information Theory, May, 2005.

27.   Li-Wei Chen and Eytan Modiano, “Efficient Routing and Wavelength Assignment for Reconfigurable WDM Networks with Wavelength Converters,” IEEE/ACM Transactions on Networking, February, 2005. Selected as one of the best papers from Infocom 2003 for fast-track publication in IEEE/ACM Transactions on Networking.

26.   Michael Neely and Eytan Modiano, “Convexity in Queues with General Inputs,” IEEE Transactions on Information Theory, May, 2005.

25.   Anand Srinivas and Eytan Modiano, “Finding Minimum Energy Disjoint Paths in Wireless Ad Hoc Networks,” ACM Wireless Networks, November, 2005. Selected to appear in a special issue dedicated to best papers from Mobicom 2003.

24.   Michael Neely, Eytan Modiano and Charlie Rohrs, “Dynamic Power Allocation and Routing for Time-Varying Wireless Networks,” IEEE Journal of Selected Areas in Communication, January, 2005.

23.   Chunmei Liu and Eytan Modiano, “On the performance of additive increase multiplicative decrease (AIMD) protocols in hybrid space-terrestrial networks,” Computer Networks, September, 2004.

22.   Li-Wei Chen and Eytan Modiano, “Dynamic Routing and Wavelength Assignment with Optical Bypass using Ring Embeddings,” Optical Switching and Networking (Elsevier), December, 2004.

21.   Aradhana Narula-Tam, Eytan Modiano and Andrew Brzezinski, “Physical Topology Design for Survivable Routing of Logical Rings in WDM-Based Networks,” IEEE Journal of Selected Areas in Communication, October, 2004.

20.   Randall Berry and Eytan Modiano, “‘The Role of Switching in Reducing the Number of Electronic Ports in WDM Networks,” IEEE Journal of Selected Areas in Communication, October, 2004.

19.   Jun Sun and Eytan Modiano, “Routing Strategies for Maximizing Throughput in LEO Satellite Networks,,” IEEE JSAC, February, 2004.

18.   Jun Sun and Eytan Modiano, “Capacity Provisioning and Failure Recovery for Low Earth Orbit Satellite Networks,” International Journal on Satellite Communications, June, 2003.

17.   Alvin Fu, Eytan Modiano, and John Tsitsiklis, “Optimal Energy Allocation and Admission Control for Communications Satellites,” IEEE Transactions on Networking, June, 2003.

16.   Michael Neely, Eytan Modiano and Charles Rohrs, “Power Allocation and Routing in Multi-Beam Satellites with Time Varying Channels,” IEEE Transactions on Networking, February, 2003.

15.   Eytan Modiano and Aradhana Narula-Tam, “Survivable lightpath routing: a new approach to the design of WDM-based networks,” IEEE Journal of Selected Areas in Communication, May 2002.

14.   Aradhana Narula-Tam, Phil Lin and Eytan Modiano, “Efficient Routing and Wavelength Assignment for Reconfigurable WDM Networks,” IEEE Journal of Selected Areas in Communication, January, 2002.

13.   Brett Schein and Eytan Modiano, “Quantifying the benefits of configurability in circuit-switched WDM ring networks with limited ports per node,” IEEE Journal on Lightwave Technology, June, 2001.

12.   Aradhana Narula-Tam and Eytan Modiano, “Dynamic Load Balancing in WDM Packet Networks with and without Wavelength Constraints,” IEEE Journal of Selected Areas in Communications, October 2000.

11.   Randy Berry and Eytan Modiano, “Reducing Electronic Multiplexing Costs in SONET/WDM Rings with Dynamically Changing Traffic,” IEEE Journal of Selected Areas in Communications, October 2000.

10.   Eytan Modiano and Richard Barry, “A Novel Medium Access Control Protocol for WDM-Based LANs and Access Networks Using a Master-Slave Scheduler,” IEEE Journal on Lightwave Technology, April 2000.

9.   Eytan Modiano and Anthony Ephremides, “Communication Protocols for Secure Distributed Computation of Binary Functions,” Information and Computation, April 2000.

8.   Angela Chiu and Eytan Modiano, “Traffic Grooming Algorithms for Reducing Electronic Multiplexing Costs in WDM Ring Networks,” IEEE Journal on Lightwave Technology, January 2000.

7.   Eytan Modiano, “An Adaptive Algorithm for Optimizing the Packet Size Used in Wireless ARQ Protocols,” Wireless Networks, August 1999.

6.   Eytan Modiano, “Random Algorithms for Scheduling Multicast Traffic in WDM Broadcast-and-Select Networks,” IEEE Transactions on Networking, July, 1999.

5.   Eytan Modiano and Richard Barry, “Architectural Considerations in the Design of WDM-based Optical Access Networks,” Computer Networks, February 1999.

4.   V.W.S. Chan, K. Hall, E. Modiano and K. Rauschenbach, “Architectures and Technologies for High-Speed Optical Data Networks,” IEEE Journal of Lightwave Technology, December 1998.

3.   Eytan Modiano and Anthony Ephremides, “Efficient Algorithms for Performing Packet Broadcasts in a Mesh Network,” IEEE Transactions on Networking, May 1996.

2.   Eytan Modiano, Jeffrey Wieselthier and Anthony Ephremides, “A Simple Analysis of Queueing Delay in a Tree Network of Discrete-Time Queues with Constant Service Times,” IEEE Transactions on Information Theory, February 1996.

1.   Eytan Modiano and Anthony Ephremides, “Communication Complexity of Secure Distributed Computation in the Presence of Noise,” IEEE Transactions on Information Theory, July 1992.

Other Papers

5.  Eytan Modiano, “Satellite Data Networks,” AIAA Journal on Aerospace Computing, Information and Communication, September, 2004.

4.  Eytan Modiano and Phil Lin, “Traffic Grooming in WDM networks,” IEEE Communications Magazine, July, 2001.

3.  Eytan Modiano and Aradhana Narula, “Mechanisms for Providing Optical Bypass in WDM-based Networks,” SPIE Optical Networks, January 2000.

2.  K. Kuznetsov, N. M. Froberg, Eytan Modiano, et. al., “A Next Generation Optical Regional Access Networks,” IEEE Communications Magazine, January, 2000.

1.  Eytan Modiano, “WDM-based Packet Networks,” (Invited Paper) IEEE Communications Magazine, March 1999.

Conference Papers

246. Xinyu Wu, Dan Wu, Eytan Modiano, “ Overload Balancing in Single-Hop Networks With Bounded Buffers ,” IFIP Networking, 2022.

245.  Xinzhe Fu, Eytan Modiano, “ Optimal Routing for Stream Learning Systems ,”  IEEE Infocom, April 2022.

244.  Vishrant Tripathi, Luca Ballotta, Luca Carlone, E. Modiano, “ Computation and Communication Co-Design for Real-Time Monitoring and Control in Multi-Agent Systems ,”  IEEE Wiopt, 2021.

243. Eray Atay, Igor Kadota, E. Modiano, “ Aging Wireless Bandits: Regret Analysis and Order-Optimal Learning Algorithm ,”  IEEE Wiopt 2021.

242. Xinzhe Fu and E. Modiano,  “ Elastic Job Scheduling with Unknown Utility Functions ,” IFIP Performance, Milan, 2021.

241. Bai Liu and E. Modiano, “ Optimal Control for Networks with Unobservable Malicious Nodes ,”  IFIP Performance, Milan, 2021.

240. Bai Liu, Qiaomin Xie,  Eytan Modiano, “ RL-QN:  A Reinforcement Learning Framework for Optimal Control of Queueing Systems ,”  ACM Sigmetrics Workshop on Reinforcement Learning in Networks and Queues (RLNQ), 2021.

239. Xinzhe Fu and E. Modiano,  “ Learning-NUM: Network Utility Maximization with Unknown Utility Functions and Queueing Delay ,  ACM MobiHoc 2021.  

238. Vishrant Tripathi and Eytan Modiano,  “ An Online Learning Approach to Optimizing Time-Varying Costs of AoI ,”  ACM MobiHoc 2021. 

237.   Igor Kadota, Muhammad Shahir Rahman, and Eytan Modiano, “ WiFresh: Age-of-Information from Theory to Implementation ,”  International Conference on Computer Communications and Networks (ICCCN), 2021.

236. Vishrant Tripathi and Eytan Modiano, “ Age Debt: A General Framework For Minimizing Age of Information ,”  IEEE Infocom Workshop on Age-of-Information, 2021.

235. Igor Kadota, Eytan Modiano, “ Age of Information in Random Access Networks with Stochastic Arrivals ,” IEEE Infocom, 2020.

234. Igor Kadota, M. Shahir Rahman, Eytan Modiano, Poster: Age of Information in Wireless Networks: from Theory to Implementation , ACM Mobicom, 2020.

233. Xinyu Wu, Dan Wu, Eytan Modiano, “ An Influence Model Approach to Failure Cascade Prediction in Large Scale Power Systems ,” IEEE American Control Conference, July, 2020.

232. X. Fu and E. Modiano, “ Fundamental Limits of Volume-based Network DoS Attacks ,” Proc. ACM Sigmetrics, Boston, MA, June 2020.

231. Vishrant Tripathi, Eytan Modiano, “ A Whittle Index Approach to Minimizing Functions of Age of Information ,” Allerton Conference on Communication, Control, and Computing, September 2019.

230. Bai Liu, Xiaomin Xie, Eytan Modiano, “ Reinforcement Learning for Optimal Control of Queueing Systems ,” Allerton Conference on Communication, Control, and Computing, September 2019.

229. Rajat Talak, Sertac Karaman, Eytan Modiano, “ A Theory of Uncertainty Variables for State Estimation and Inference ,” Allerton Conference on Communication, Control, and Computing, September 2019.

228. Rajat Talak, Eytan Modiano, “ Age-Delay Tradeoffs in Single Server Systems ,” IEEE International Symposium on Information Theory, Paris, France, July, 2019.

227. Rajat Talak, Sertac Karaman, Eytan Modiano, “ When a Heavy Tailed Service Minimizes Age of Information ,” IEEE International Symposium on Information Theory, Paris, France, July, 2019.

226. Qingkai Liang, Eytan Modiano, “ Optimal Network Control with Adversarial Uncontrollable Nodes ,” ACM MobiHoc, Catania, Italy, June 2019.

225. Igor Kadota, Eytan Modiano, “ Minimizing the Age of Information in Wireless Networks with Stochastic Arrivals ,” ACM MobiHoc, June 2019.

224. Maotong Xu, Jelena Diakonikolas, Suresh Subramaniam, Eytan Modiano, “ A Hierarchical WDM-based Scalable Data Center Network Architecture ,” IEEE International Conference on Communications (ICC), Shanghai, China, June 2019.

223. Maotong Xu, Min Tian, Eytan Modiano, Suresh Subramaniam, “ RHODA Topology Configuration Using Bayesian Optimization

222.   Anurag Rai, Rahul Singh and Eytan Modiano, “ A Distributed Algorithm for Throughput Optimal Routing in Overlay Networks ,”  IFIP Networking 2019, Warsaw, Poland, May 2019.

221.   Qingkai Liang and Eytan Modiano, “ Optimal Network Control in Partially-Controllable Networks ,”  IEEE Infocom, Paris, April 2019.

220.   Xinzhe Fu and Eytan Modiano, “ Network Interdiction Using Adversarial Traffic Flows ,”  IEEE Infocom, Paris, April 2019.

219.   Vishrant Tripathi, Rajat Talak, Eytan Modiano, “ Age Optimal Information Gathering and Dissemination on Graphs ,”  IEEE Infocom, Paris, April 2019.

218.   Jianan Zhang, Hyang-Won Lee, Eytan Modiano, “ On the Robustness of Distributed Computing Networks ,”  DRCN 2019, Coimbra, Portugal, March, 2019.

217.   Hyang-Won Lee, Jianan Zhang and Eytan Modiano, “ Data-driven Localization and Estimation of Disturbance in the Interconnected Power System ,”  IEEE Smartgridcomm, October, 2018.

216.   Jianan Zhang and Eytan Modiano, “ Joint Frequency Regulation and Economic Dispatch Using Limited Communication ,”  IEEE Smartgridcomm, October, 2018.

215.   Rajat Talak, Sertac Karaman, Eytan Modiano, “ Scheduling Policies for Age Minimization in Wireless Networks with Unknown Channel State ,”  IEEE International Symposium on Information Theory, July 2018.

214.   Thomas Stahlbuhk, Brooke Shrader, Eytan Modiano, “ Online Learning Algorithms for Minimizing Queue Length Regret ,”  IEEE International Symposium on Information Theory, July 2018.

213.   Rajat Talak, Sertac Karaman, Eytan Modiano, “ Distributed Scheduling Algorithms for Optimizing Information Freshness in Wireless Networks ,”  IEEE SPAWC, Kalamata, Greece, June, 2018.

212.   Rajat Talak, Sertac Karaman, Eytan Modiano, “ Optimizing Information Freshness in Wireless Networks under General Interference Constraints ,”  ACM MobiHoc 2018, Los Angeles, CA, June 2018.

211.   Thomas Stahlbuhk, Brooke Shrader, Eytan Modiano, “ Learning Algorithms for Scheduling in Wireless Networks with Unknown Channel Statistics ,”  ACM MobiHoc, June 2018.

210.   Khashayar Kamran, Jianan Zhang, Edmund Yeh, Eytan Modiano, “ Robustness of Interdependent Geometric Networks Under Inhomogeneous Failures ,”  Workshop on Spatial Stochastic Models for Wireless Networks (SpaSWiN), Shanghai, China, May 2018.

209.   Rajat Talak, Sertac Karaman, Eytan Modiano, “ Optimizing Age of Information in Wireless Networks with Perfect Channel State Information ,”  Wiopt 2018, Shanghai, China, May 2018.

208.   Abhishek Sinha, Eytan Modiano, “ Network Utility Maximization with Heterogeneous Traffic Flows ,”  Wiopt 2018, Shanghai, China, May 2018.

207.   Qingkai Liang, Eytan Modiano, “ Minimizing Queue Length Regret Under Adversarial Network Models ,”  ACM Sigmetrics, 2018.

206.   Jianan Zhang, Abhishek Sinha, Jaime Llorca, Anonia Tulino, Eytan Modiano, “ Optimal Control of Distributed Computing Networks with Mixed-Cast Traffic Flows ,”  IEEE Infocom, Honolulu, HI, April 2018.

205.   Qingkai Liang, Eytan Modiano, “ Network Utility Maximization in Adversarial Environments ,”  IEEE Infocom, Honolulu, HI, April 2018.

204.   Igor Kadota, Abhishek Sinha, Eytan Modiano, “ Optimizing Age of Information in Wireless Networks with Throughput Constraints ,”  IEEE Infocom, Honolulu, HI, April 2018.

203.   QIngkai Liang, Verina (Fanyu) Que, Eytan Modiano, “ Accelerated Primal-Dual Policy Optimization for Safe Reinforcement Learning ,”  NIPS workshop on “Transparent and interpretable machine learning in safety critical environments,”December 2017.

202.   Rahul Singh, Xueying Guo,Eytan Modiano, “ Risk-Sensitive Optimal Control of Queues ,”  IEEE Conference on Decision and Control (CDC), December 2017.

201.   Rajat Talak, Sertac Karaman, Eytan Modiano, “ Minimizing Age of Information in Multi-Hop Wireless Networks ,”  Allerton Conference on Communication, Control, and Computing, September 2017.

200.   Abhishek Sinha, Eytan Modiano, “ Throughput-Optimal Broadcast in Wireless Networks with Point-to-Multipoint Transmissions ,”  ACM MobiHoc, Madras, India, July 2017.

199.   Rajat Talak, Sertac Karaman, Eytan Modiano, “ Capacity and delay scaling for broadcast transmission in highly mobile wireless networks ,”  ACM MobiHoc, Madras, India, July 2017.

198.5 . Y.-P. Hsu, E. Modiano, and L. Duan, “ Age of Information: Design and Analysis of Optimal Scheduling Algorithms ,”  IEEE International Symposium on Information Theory (ISIT), 2017.

198.   Qingkai Liang and Eytan Modiano, “ Coflow Scheduling in Input-Queued Switches: Optimal Delay Scaling and Algorithms ,”  IEEE Infocom, Atlanta, GA, May 2017.

197.   Jianan Zhang and Eytan Modiano, “ Robust Routing in Interdependent Networks ,”  IEEE Infocom, Atlanta, GA, May 2017.

196.   Abhishek Sinha, Eytan Modiano, “ Optimal Control for Generalized Network Flow Problems ,”  IEEE Infocom, Atlanta, GA, May 2017.

195.   Rajat Talak*, Sertac Karaman, Eytan Modiano, “ Speed Limits in Autonomous Vehicular Networks due to Communication Constraints ,”  IEEE Conference on Decision and Control (CDC), Las Vegas, NV, December 2016.

194.   Marzieh Parandehgheibi*, Konstantin Turitsyn, Eytan Modiano, “ Distributed Frequency Control in Power Grids Under Limited Communication ,”  IEEE Conference on Decision and Control (CDC), Las Vegas, NV, December 2016.

193.   Igor Kadota, Elif Uysal-Biyikoglu, Rahul Singh, Eytan Modiano, “ Minimizing Age of Information in Broadcast Wireless Networks ,”  Allerton Allerton Conference on Communication, Control, and Computing, September 2016.

192.   Jianan Zhang, Edmund Yeh, Eytan Modiano, “ Robustness of Interdependent Random Geometric Networks ,”  Allerton Conference on Communication, Control, and Computing, September 2016.

191.   Abhishek Sinha, Leandros Tassiulas, Eytan Modiano, “ Throughput-Optimal Broadcast in Wireless Networks with Dynamic Topology ,”  ACM MobiHoc’16, Paderborn, Germany, July, 2016. (winner of best paper award)

190.   Abishek Sinha, Georgios Paschos, Eytan Modiano, “ Throughput-Optimal Multi-hop Broadcast Algorithms ,”  ACM MobiHoc’16, Paderborn, Germany, July, 2016.

189.   Thomas Stahlbuhk, Brooke Shrader, Eytan Modiano, “ Throughput Maximization in Uncooperative Spectrum Sharing Networks ,”  IEEE International Symposium on Information Theory, Barcelona, Spain, July 2016.

188.   Thomas Stahlbuhk, Brooke Shrader, Eytan Modiano, “ Topology Control for Wireless Networks with Highly-Directional Antennas ,”  IEEE Wiopt, Tempe, Arizona, May, 2016.

187.   Qingkai Liang, H.W. Lee, Eytan Modiano, “ Robust Design of Spectrum-Sharing Networks ,”  IEEE Wiopt, Tempe, Arizona, May, 2016.

186.   Hossein Shokri-Ghadikolae, Carlo Fischione and Eytan Modiano, “ On the Accuracy of Interference Models in Wireless Communications ,”  IEEE International Conference on Communications (ICC), 2016.

185.   Qingkai Liang and Eytan Modiano, “ Survivability in Time-varying Networks ,”  IEEE Infocom, San Francisco, CA, April 2016.

184.   Kyu S. Kim, Chih-Ping Li, Igor Kadota, Eytan Modiano, “ Optimal Scheduling of Real-Time Traffic in Wireless Networks with Delayed Feedback ,”  Allerton conference on Communication, Control, and Computing, September 2015.

183.   Marzieh Parandehgheibi, Eytan Modiano, “ Modeling the Impact of Communication Loss on the Power Grid Under Emergency Control ,”  IEEE SmartGridComm, Miami, FL, Nov. 2015.

182.   Anurag Rai, Chih-ping Li, Georgios Paschos, Eytan Modiano, “ Loop-Free Backpressure Routing Using Link-Reversal Algorithms ,”  Proceedings of the ACM MobiHoc, July 2015.

181.   Longbo Huang, Eytan Modiano, “ Optimizing Age of Information in a Multiclass Queueing System ,”  Proceedings of IEEE ISIT 2015, Hong Kong, Jun 2015.

180.   M. Johnston, E. Modiano, “ A New Look at Wireless Scheduling with Delayed Information ,”  Proceedings of IEEE ISIT 2015, Hong Kong, June 2015.

179.   M. Johnston, E. Modiano, “ Scheduling over Time Varying Channels with Hidden State Information ,”  Proceedings of IEEE ISIT 2015, Hong Kong, June 2015.

178.   M. Johnston and E. Modiano, “ Controller Placement for Maximum Throughput Under Delayed CSI ,”  IEEE Wiopt, Mombai, India, May 2015.

177.   A. Sinha, G. Paschos, C. P. Li, and E. Modiano, “ Throughput Optimal Broadcast on Directed Acyclic Graphs ,”  IEEE Infocom, Hong Kong, April 2015.

176.   J. Zheng and E. Modiano, “ Enhancing Network Robustness via Shielding ,”  IEEE Design of Reliable Communication Networks, Kansas City, March 2015.

175.   H. W. Lee and E. Modiano, “ Robust Design of Cognitive Radio Networks ,”  Information and Communication Technology Convergence (ICTC), 2014.

174.   Greg Kuperman and Eytan Modiano, “ Disjoint Path Protection in Multi-Hop Wireless Networks with Interference Constraints ,”  IEEE Globecom, Austin, TX, December 2014.

173.   Marzieh Parandehgheibi, Eytan Modiano, David Hay, “ Mitigating Cascading Failures in Interdependent Power Grids and Communication Networks ,”  IEEE Smartgridcomm, Venice, Italy, November 2014.

172.   Georgios Paschos and Eytan Modiano, “ Throughput optimal routing in overlay networks ,”  Allerton conference on Communication, Control, and Computing, September 2014.

171.   Nathan Jones, George Paschos, Brooke Shrader, Eytan Modiano, “ An overlay architecture for Throughput Optimal Multipath Routing ,”  ACM MobiHoc, August 2014.

170.   Matt Johnston, Eytan Modiano, Yuri Polyanskiy, “ Opportunistic Scheduling with Limited Channel State Information: A Rate Distortion Approach ,”  IEEE International Symposium on Information Theory, Honolulu, HI, July 2014.

169.   Chih-Ping Li, Georgios Paschos, Eytan Modiano, Leandros Tassiulas, “ Dynamic Overload Balancing in Server Farms ,”  Networking 2014, Trondheim, Norway, June, 2014.

168.   Hulya Seferonglu and Eytan Modiano, “ TCP-Aware Backpressure Routing and Scheduling ,”  Information Theory and Applications, San Diego, CA, February 2014.

167.   Mihalis Markakis, Eytan Modiano, John Tsitsiklis, “ Delay Stability of Back-Pressure Policies in the presence of Heavy-Tailed Traffic ,”  Information Theory and Applications, San Diego, CA, February 2014.

166.   Kyu Soeb Kim, Chih-ping Li, Eytan Modiano, “ Scheduling Multicast Traffic with Deadlines in Wireless Networks ,”  IEEE Infocom, Toronto, CA, April 2014.

165.   Georgios Paschos, Chih-ping Li, Eytan Modiano, Kostas Choumas, Thanasis Korakis, “ A Demonstration of Multirate Multicast Over an 802.11 Mesh Network ,”  IEEE Infocom, Toronto, CA, April 2014.

164.   Sebastian Neumayer, Eytan Modiano, “ Assessing the Effect of Geographically Correlated Failures on Interconnected Power-Communication Networks ,”  IEEE SmartGridComm, 2013.

163.   Marzieh Parandehgheibi, Eytan Modiano, “ Robustness of Interdependent Networks: The case of communication networks and the power grid ,”  IEEE Globecom, December 2013.

162.   Matt Johnston, Eytan Modiano, “ Optimal Channel Probing in Communication Systems: The Two-Channel Case ,”  IEEE Globecom, December 2013.

161.   Mihalis Markakis, Eytan Modiano, John N. Tsitsiklis, “ Delay Analysis of the Max-Weight Policy under Heavy-Tailed Traffic via Fluid Approximations ,”  Allerton Conference, October 2013.

160.   Matthew Johnston, Isaac Keslassy, Eytan Modiano, “ Channel Probing in Communication Systems: Myopic Policies Are Not Always Optimal ,”  IEEE International Symposium on Information Theory, July 2013.

159.   Krishna P Jagannathan, Libin Jiang, Palthya Lakshma Naik, Eytan Modiano, “ Scheduling Strategies to Mitigate the Impact of Bursty Traffic in Wireless Networks ,”  11th International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks Wiopt 2013, Japan, May 2013. (Winner – Best Paper Award).

158.   Hulya Seferoglu and Eytan Modiano, “ Diff-Max: Separation of Routing and Scheduling in Backpressure-Based Wireless Networks ,”  IEEE Infocom, Turin, Italy, April 2013.

157.   Chih-Ping Li, Eytan Modiano, “ Receiver-Based Flow Control for Networks in Overload ,”  IEEE Infocom, Turin, Italy, April 2013.

156.   Nathan Jones, Brooke Shrader, Eytan Modiano, “ Distributed CSMA with Pairwise Coding ,”  IEEE Infocom, Turin, Italy, April 2013.

155.   Greg Kuperman and Eytan Modiano, “ Network Protection with Guaranteed Recovery Times using Recovery Domains ,”  IEEE Infocom, Turin, Italy, April 2013.

154.   Greg Kuperman and Eytan Modiano, “ Providing Protection in Multi-Hop Wireless Networks ,”  IEEE Infocom, Turin, Italy, April 2013.

153.   Greg Kuperman, Eytan Modiano, Aradhana Narula-Tam, “ Network Protection with Multiple Availability Guarantees ,”  IEEE ICC workshop on New Trends in Optical Networks Survivability, June 2012.

152.   Nathaniel Jones, Brooke Shrader, Eytan Modiano, “ Optimal Routing and Scheduling for a Simple Network Coding Scheme ,”  IEEE Infocom, Orlando, Fl, March, 2012.

151.   Mihalis Markakis, Eytan Modiano, John Tsitsiklis, “ Max-Weight Scheduling in Networks with Heavy-Tailed Traffic ,”  IEEE Infocom, Orlando, Fl, March, 2012.

150.   Guner Celik and Eytan Modiano, “ Scheduling in Networks with Time-Varying Channels and Reconfiguration Delay ,”  IEEE Infocom, Orlando, Fl, March, 2012.

149.   Sebastian Neumayer, Alon Efrat, Eytan Modiano, “ Geographic Max-Flow and Min-cut Under a Circular Disk Failure Model ,”  IEEE Infocom (MC), Orlando, Fl, March, 2012.

148.   Marzieh Parandehgheibi, Hyang-Won Lee, and Eytan Modiano, “ Survivable Paths in Multi-Layer Networks ,”  Conference on Information Science and Systems, March, 2012.

147.   Greg Kuperman, Eytan Modiano, and Aradhana Narula-Tam, “ Partial Protection in Networks with Backup Capacity Sharing ,”  Optical Fiber Communications Conference (OFC), Anaheim, CA, March, 2012.

146.   Krishna Jagannathan, Libin Jiang, Eytan Modiano, “ On Scheduling Algorithms Robust to Heavy-Tailed Traffic ,”  Information Theory and Applications (ITA), San Diego, CA, February 2012.

145.   M. Johnston, H.W. Lee, E. Modiano, “ Robust Network Design for Stochastic Traffic Demands ,”  IEEE Globecom, Next Generation Networking Symposium, Houston, TX, December 2011.

144.   S. Neumayer, E. Modiano, “ Network Reliability Under Random Circular Cuts ,”  IEEE Globecom, Optical Networks and Systems Symposium, Houston, TX, December 2011.

143.   H.W. Lee, K. Lee, E. Modiano, “ Maximizing Reliability in WDM Networks through Lightpath Routing ,”  IEEE Globecom, Optical Networks and Systems Symposium, Houston, TX, December 2011.

142.   Guner Celik, Sem Borst, Eytan Modiano, Phil Whiting, “ Variable Frame Based Max-Weight Algorithms for Networks with Switchover Delay ,”  IEEE International Symposium on Information Theory, St. Petersburgh, Russia, August 2011.

141.   Krishna Jaganathan, Ishai Menache, Eytan Modiano, and Gil Zussman, “ Non-cooperative Spectrum Access – The Dedicated vs. Free Spectrum Choice ,”  ACM MOBIHOC’11, May 2011.

140.   Krishna Jagannathan, Shie Mannor, Ishai Menache, Eytan Modiano, “ A State Action Frequency Approach to Throughput Maximization over Uncertain Wireless Channels ,”  IEEE Infocom (Mini-conference), Shanghai, China, April 2011.

139.   Guner Celik, Long B. Le, Eytan Modiano, “ Scheduling in Parallel Queues with Randomly Varying Connectivity and Switchover Delay ,”  IEEE Infocom (Mini-conference), Shanghai, China, April 2011.

138.   Gregory Kuperman, Eytan Modiano, Aradhana Narula-Tam, “ Analysis and Algorithms for Partial Protection in Mesh Networks ,”  IEEE Infocom (Mini-conference), Shanghai, China, April 2011.

137.   Matthew Johnston, Hyang-Won Lee, Eytan Modiano, “ A Robust Optimization Approach to Backup Network Design with Random Failures ,”  IEEE Infocom, Shanghai, China, April 2011.

136.   Krishna Jagannathan, Mihalis Markakis, Eytan Modiano, John Tsitsiklis, “ Queue Length Asymptotics for Generalized Max-Weight Scheduling in the presence of Heavy-Tailed Traffic ,”  IEEE Infocom, Shanghai, China, April 2011.

135.   Guner Celik and Eytan Modiano, “ Dynamic Vehicle Routing for Data Gathering in Wireless Networks ,”  In Proc. IEEE CDC’10, Dec. 2010..***

134.   Long B. Le, Eytan Modiano, Changhee Joo, and Ness B. Shroff, “ Longest-queue-first scheduling under the SINR interference model ,”  ACM MobiHoc, September 2010..***

133.   Krishna Jagannathan, Mihalis Markakis, Eytan Modiano, John Tsitsiklis, “ Throughput Optimal Scheduling in the Presence of Heavy-Tailed Traffic ,”  Allerton Conference on Communication, Control, and Computing, September 2010..**

132.   Delia Ciullo, Guner Celik, Eytan Modiano, “ Minimizing Transmission Energy in Sensor Networks via Trajectory Control ,”  IEEE Wiopt 2010, Avignon, France, June 2010, (10 pages; CD proceedings – page numbers not available).

131.   Sebastian Neumayer and Eytan Modiano, “ Network Reliability with Geographically Correlated Failures ,”  IEEE Infocom 2010, San Diego, CA, March 2010, (9 pages; CD proceedings – page numbers not available).**

130.   Long Le, Eytan Modiano, Ness Shroff, “ Optimal Control of Wireless Networks with Finite Buffers ,”  IEEE Infocom 2010, San Diego, CA, March 2010, (9 pages; CD proceedings – page numbers not available).

129.   Kayi Lee, Hyang-Won Lee, Eytan Modiano, “ Reliability in Layered Network with Random Link Failures ,”  IEEE Infocom 2010, San Diego, CA, March 2010, (9 pages; CD proceedings – page numbers not available).**

128.   Krishna Jagannathan, Eytan Modiano, “ The Impact of Queue length Information on Buffer Overflow in Parallel Queues ,”  Allerton Conference on Communication, Control, and Computing, September 2009, pgs. 1103 -1110 **

127.   Mihalis Markakis, Eytan Modiano, John Tsitsiklis, “ Scheduling Policies for Single-Hop with Heavy-Tailed Traffic ,”  Allerton Conference on Communication, Control, and Computing, September 2009, pgs. 112 – 120..**

126.   Dan Kan, Aradhana Narula-Tam, Eytan Modiano, “ Lightpath Routing and Capacity Assignment for Survivable IP-over-WDM Networks ,”  DRCN 2009, Alexandria, VA October 2009, pgs. 37 -44..**

125.   Mehdi Ansari, Alireza Bayesteh, Eytan Modiano, “ Opportunistic Scheduling in Large Scale Wireless Networks ,”  IEEE International Symposium on Information Theory, Seoul, Korea, June 2009, pgs. 1624 – 1628.

124.   Hyang-Won Lee, Eytan Modiano and Long Bao Le, “ Distributed Throughput Maximization in Wireless Networks via Random Power Allocation ,”  IEEE Wiopt, Seoul, Korea, June 2009. (9 pages; CD proceedings – page numbers not available).

123.   Wajahat Khan, Eytan Modiano, Long Le, “ Autonomous Routing Algorithms for Networks with Wide-Spread Failures ,”  IEEE MILCOM, Boston, MA, October 2009. (6 pages; CD proceedings – page numbers not available).**

122.   Guner Celik and Eytan Modiano, “ Random Access Wireless Networks with Controlled Mobility ,”  IEEE Med-Hoc-Nets, Haifa, Israel, June 2009, pgs. 8 – 14.**

121.   Hyang-Won Lee and Eytan Modiano, “ Diverse Routing in Networks with Probabilistic Failures ,”  IEEE Infocom, April 2009, pgs. 1035 – 1043.

120.   Kayi Lee and Eytan Modiano, “ Cross-layer Survivability in WDM-based Networks ,”  IEEE Infocom, April 2009, pgs. 1017 -1025..**

119.   Krishna Jagannathan, Eytan Modiano, Lizhong Zheng, “ On the Trade-off between Control Rate and Congestion in Single Server Systems ,”  IEEE Infocom, April 2009, pgs. 271 – 279.**

118.   Sebastian Neumayer, Gil Zussman, Rueven Cohen, Eytan Modiano, “ Assessing the Vulnerability of the Fiber Infrastructure to Disasters ,”  IEEE Infocom, April 2009, pgs. 1566 – 1574.**

117.   Long Le, Krishna Jagannathan and Eytan Modiano, “ Delay analysis of max-weight scheduling in wireless ad hoc networks ,”  Conference on Information Science and Systems, Baltimore, MD, March, 2009, pgs. 389 – 394.**

116.   Krishna Jagannathan, Eytan Modiano, Lizhong Zheng, “ Effective Resource Allocation in a Queue: How Much Control is Necessary? ,”  Allerton Conference on Communication, Control, and Computing, September 2008, pgs. 508 – 515.**

115.   Sebastian Neumayer, Gil Zussman, Rueven Cohen, Eytan Modiano, “ Assessing the Impact of Geographically Correlated Network Failures ,”  IEEE MILCOM, November 2008. (6 pages; CD proceedings – page numbers not available).**

114.   Emily Craparo, Jonathan P. How, and Eytan Modiano, “ Simultaneous Placement and Assignment for Exploration in Mobile Backbone Networks ,”  IEEE conference on Decision and Control (CDC), November 2008, pgs. 1696 – 1701 **

113.   Anand Srinivas and Eytan Modiano, “ Joint node placement and assignment for throughput optimization in mobile backbone networks ,”  IEEE INFOCOM’08, pp. 1130 – 1138, Phoenix, AZ, Apr. 2008, pgs. 1130 – 1138.**

112.   Guner Celik, Gil Zussman, Wajahat Khan and Eytan Modiano, “ MAC for Networks with Multipacket Reception Capability and Spatially Distributed Nodes ,”  IEEE INFOCOM’08, Phoenix, AZ, Apr. 2008, pgs. 1436 – 1444.**

111.   Gil Zussman, Andrew Brzezinski, and Eytan Modiano, “ Multihop Local Pooling for Distributed Throughput Maximization in Wireless Networks ,”  IEEE INFOCOM’08, Phoenix, AZ, Apr. 2008, pgs 1139 – 1147.**

110.   Emily Craparo, Jonathan How and Eytan Modiano, “ Optimization of Mobile Backbone Networks: Improved Algorithms and Approximation ,”  IEEE American Control Conference, Seattle, WA, June 2008, pgs. 2016 – 2021.**

109.   Atilla Eryilmaz, Asuman Ozdaglar, Devavrat Shah, Eytan Modiano, “ Imperfect Randomized Algorithms for the Optimal Control of Wireless Networks ,”  Conference on Information Science and Systems, Princeton, NJ, March, 2008, pgs. 932 – 937.

108.   Anand Srinivas and Eytan Modiano, “ Optimal Path Planning for Mobile Backbone Networks ,”  Conference on Information Science and Systems, Princeton, NJ, March, 2008, pgs. 913 – 918.

107.   Kayi Lee and Eytan Modiano, “ Cross-layer Survivability in WDM Networks with Multiple Failures ,”  IEEE Optical Fiber Communications Conference, San Diego, CA February, 2008 (3 pages; CD proceedings – page numbers not available).

106.   Andrew Brzezinski, Gil Zussman and Eytan Modiano, “ Local Pooling Conditions for Joint Routing and Scheduling ,”  Workshop on Information Theory and Applications, pp. 499 – 506, La Jolla, CA, January, 2008, pgs. 499 – 506.

105.   Murtaza Zafer and Eytan Modiano, “ Minimum Energy Transmission over a Wireless Fading Channel with Packet Deadlines ,”  Proceedings of IEEE Conference on Decision and Control (CDC), New Orleans, LA, December, 2007, pgs. 1148 – 1155.**

104.   Atilla Eryilmaz, Asuman Ozdaglar, Eytan Modiano, “ Polynomial Complexity Algorithms for Full Utilization of Multi-hop Wireless Networks ,”  IEEE Infocom, Anchorage, AK, April, 2007, pgs. 499 – 507.

103.   Murtaza Zafer and Eytan Modiano, “ Delay Constrained Energy Efficient Data Transmission over a Wireless Fading Channel ,”  Workshop on Information Theory and Application, University of California, San Diego, CA, February, 2007, pgs. 289 – 298.**

102.   Atilla Eryilmaz, Eytan Modiano, Asuman Ozdaglar, “ Randomized Algorithms for Throughput-Optimality and Fairness in Wireless Networks ,”  Proceedings of IEEE Conference on Decision and Control (CDC), San Diego, CA, December, 2006, pgs. 1936 – 1941.

101.   Anand Srinivas, Gil Zussman, and Eytan Modiano, “ Distributed Mobile Disk Cover – A Building Block for Mobile Backbone Networks ,”  Proc. Allerton Conf. on Communication, Control, and Computing, Allerton, IL, September 2006, (9 pages; CD proceedings – page numbers not available).**

100.   Krishna Jagannathan, Sem Borst, Phil Whiting, Eytan Modiano, “ Scheduling of Multi-Antenna Broadcast Systems with Heterogeneous Users ,”  Allerton Conference on Communication, Control and Computing, Allerton, IL, September 2006, (10 pages; CD proceedings – page numbers not available).**

99.   Andrew Brzezinski, Gil Zussman, and Eytan Modiano, “ Enabling Distributed Throughput Maximization in Wireless Mesh Networks – A Partitioning Approach ,”  Proceedings of ACM MOBICOM’06, Los Angeles, CA, Sep. 2006, (12 pages; CD proceedings – page numbers not available).**

98.   Eytan Modiano, Devavrat Shah, and Gil Zussman, “ Maximizing Throughput in Wireless Networks via Gossiping ,”  Proc. ACM SIGMETRICS / IFIP Performance’06, Saint-Malo, France, June 2006, (12 pages; CD proceedings – page numbers not available). (best paper award)

97.   Anand Srinivas, Gil Zussman, and Eytan Modiano, “ Mobile Backbone Networks – Construction and Maintenance ,”  Proc. ACM MOBIHOC’06, Florence, Italy, May 2006, (12 pages; CD proceedings – page numbers not available).**

96.   Andrew Brzezinski and Eytan Modiano, “ Achieving 100% throughput in reconfigurable optical networks ,”  IEEE INFOCOM 2006 High-Speed Networking Workshop, Barcelona, Spain, April 2006, (5 pages; CD proceedings – page numbers not available).**

95.   Krishna P. Jagannathan, Sem Borst, Phil Whiting, Eytan Modiano, “ Efficient scheduling of multi-user multi-antenna systems ,”  Proceedings of WiOpt 2006, Boston, MA, April 2006, (8 pages; CD proceedings – page numbers not available).**

94.   Andrew Brzezinski and Eytan Modiano, “ Greedy weighted matching for scheduling the input-queued switch ,”  Conference on Information Sciences and Systems (CISS), Princeton, NJ, March 2006, pgs. 1738 – 1743.**

93.   Murtaza Zafer and Eytan Modiano, “ Optimal Adaptive Data Transmission over a Fading Channel with Deadline and Power Constraints ,”  Conference on Information Sciences and Systems (CISS), Princeton, New Jersey, March 2006, pgs. 931 – 937.**

92.   Li-Wei Chen and E. Modiano, “ A Geometric Approach to Capacity Provisioning in WDM Networks with Dynamic Traffic ,”  Conference on Information Science and Systems (CISS), Princeton, NJ, March, 2006, pgs. 1676 – 1683, **

91.   Jun Sun and Eytan Modiano, “ Channel Allocation Using Pricing in Satellite Networks ,”  Conference on Information Science and Systems (CISS), Princeton, NJ, March, 2006, pgs. 182 – 187.**

90.   Jun Sun, Jay Gao, Shervin Shambayatti and Eytan Modiano, “ Ka-Band Link Optimization with Rate Adaptation ,”  IEEE Aerospace Conference, Big Sky, MN, March, 2006. (7 pages; CD proceedings – page numbers not available).

89.   Alessandro Tarello, Eytan Modiano and Jay Gao, “ Energy efficient transmission scheduling over Mars proximity links ,”  IEEE Aerospace Conference, Big Sky, MN, March, 2006. (10 pages; CD proceedings – page numbers not available).

88.   A. Brzezinski and E. Modiano, “ RWA decompositions for optimal throughput in reconfigurable optical networks ,”  INFORMS Telecommunications Conference, Dallas, TX, March 2006 (3 pages; CD proceedings – page numbers not available).**

87.   Li Wei Chen and E. Modiano, “ Geometric Capacity Provisioning for Wavelength Switched WDM Networks ,”  Workshop on Information Theory and Application, University of California, San Diego, CA, February, 2006. (8 pages; CD proceedings – page numbers not available).**

86.   Murtaza Zafer and Eytan Modiano, “ Joint Scheduling of Rate-guaranteed and Best-effort Services over a Wireless Channel ,”  IEEE Conference on Decision and Control, Seville, Spain, December, 2005, pgs. 6022–6027.**

85.   Jun Sun and Eytan Modiano, “ Opportunistic Power Allocation for Fading Channels with Non-cooperative Users and Random Access ,”  IEEE BroadNets – Wireless Networking Symposium, Boston, MA, October, 2005, pgs. 397–405.**

84.   Li Wei Chen and Eytan Modiano, “ Uniform vs. Non-uniform Band Switching in WDM Networks ,”  IEEE BroadNets-Optical Networking Symposium, Boston, MA, October, 2005, pgs. 219– 228.**

83.   Sonia Jain and Eytan Modiano, “ Buffer Management Schemes for Enhanced TCP Performance over Satellite Links ,”  IEEE MILCOM, Atlantic City, NJ, October 2005 (8 pages; CD proceedings – page numbers not available).**

82.   Murtaza Zafer and Eytan Modiano, “ Continuous-time Optimal Rate Control for Delay Constrained Data Transmission ,”  Allerton Conference on Communications, Control and Computing, Allerton, IL, September, 2005 (10 pages; CD proceedings – page numbers not available).**

81.   Alessandro Tarello, Eytan Modiano, Jun Sun, Murtaza Zafer, “ Minimum Energy Transmission Scheduling subject to Deadline Constraints ,”  IEEE Wiopt, Trentino, Italy, April, 2005, pgs. 67–76. (Winner of best student paper award).**

80.   Amir Khandani, Eytan Modiano, Jinane Abounadi, Lizhong Zheng, “ Reliability and Route Diversity in Wireless Networks ,”  Conference on Information Science and System, Baltimore, MD, March, 2005, (8 pages; CD proceedings – page numbers not available).**

79.   Andrew Brzezinski, Iraj Saniee, Indra Widjaja, Eytan Modiano, “ Flow Control and Congestion Management for Distributed Scheduling of Burst Transmissions in Time-Domain Wavelength Interleaved Networks ,”  IEEE/OSA Optical Fiber Conference (OFC), Anaheim, CA, March, 2005, pgs. WC4-1–WC4-3.

78.   Andrew Brzezinski and Eytan Modiano, “ Dynamic Reconfiguration and Routing Algorithms for IP-over-WDM Networks with Stochastic Traffic ,”  IEEE Infocom 2005, Miami, FL, March, 2005, pgs. 6–11.**

77.   Murtaza Zafer and Eytan Modiano, “ A Calculus Approach to Minimum Energy Transmission Policies with Quality of Service Guarantees ,”  IEEE Infocom 2005, Miami, FL, March, 2005, pgs. 548–559.**

76.   Michael Neely and Eytan Modiano, “ Fairness and optimal stochastic control for heterogeneous networks ,”  IEEE Infocom 2005, Miami, FL, March, 2005, pgs. 1723 – 1734.**

75.   Aradhana Narula-Tam, Thomas G. Macdonald, Eytan Modiano, and Leslie Servi, “ A Dynamic Resource Allocation Strategy for Satellite Communications ,”  IEEE MILCOM, Monterey, CA, October, 2004, pgs. 1415 – 1421.

74.   Li-Wei Chen, Poompat Saengudomlert and Eytan Modiano, “ Optimal Waveband Switching in WDM Networks ,”  IEEE International Conference on Communication (ICC), Paris, France, June, 2004, pgs. 1604 – 1608.**

73.   Michael Neely and Eytan Modiano, “ Logarithmic Delay for NxN Packet Switches ,”  IEEE Workshop on High performance Switching and Routing (HPSR 2004), Phoenix, AZ, April, 2004, pgs. 3–9.**

72.   Li-Wei Chen and Eytan Modiano, “ Dynamic Routing and Wavelength Assignment with Optical Bypass using Ring Embeddings ,”  IEEE Workshop on High performance Switching and Routing (HPSR 2004), Phoenix, Az, April, 2004, pgs. 119–125.**

71.   Randall Berry and Eytan Modiano, “ On the Benefits of Tunability in Reducing Electronic Port Counts in WDM/TDM Networks ,”  IEEE Infocom, Hong Kong, March 2004, pgs. 1340–1351.

70.   Andrew Brzezinski and Eytan Modiano, “ A new look at dynamic traffic scheduling in WDM networks with transceiver tuning latency ,”  Informs Telecommunications Conference, Boca Raton, FL, March 2004, pgs. 25–26.**

69.   Chunmei Liu and Eytan Modiano, “ Packet Scheduling with Window Service Constraints ,”  Conference on Information Science and System, Princeton, NJ, March, 2004, pgs. 178–184.**

68.   Jun Sun, Eytan Modiano, and Lizhong Zheng, “ A Novel Auction Algorithm for Fair Allocation of a Wireless Fading Channel ,”  Conference on Information Science and System, Princeton, NJ, March, 2004, pgs. 1377–1383.**

67.   Murtaza Zafer and Eytan Modiano, “ Impact of Interference and Channel Assignment on Blocking Probability in Wireless Networks ,”  Conference on Information Science and System, Princeton, NJ, March, 2004, pgs. 430–436.**

66.   Chunmei Liu and Eytan Modiano, “ An Analysis of TCP over Random Access Satellite Links ,”  IEEE Wireless Communications and Networking Conference (WCNC), Atlanta, GA, February, 2004, pgs. 2033–2040..**

65.   Randall Berry and Eytan Modiano, “ Using tunable optical transceivers for reducing the number of ports in WDM/TDM Networks ,”  IEEE/OSA Optical Fiber Conference (OFC), Los Angeles, CA, February, 2004, pgs. 23–27.

64.   Aradhana Narula-Tam, Eytan Modiano and Andrew Brzezinski, “ Physical Topology Design for Survivable Routiing of Logical Rings in WDM-based Networks ,”  IEEE Globecom, San francisco, CA, December, 2003, pgs. 2552–2557.

63.   Jun Sun, Lizhong Zheng and Eytan Modiano, “ Wireless Channel Allocation Using an Auction Algorithm ,”  Allerton Conference on Communications, Control and Computing, October, 2003, pgs. 1114–1123..**

62.   Amir Khandani, Jinane Abounadi, Eytan Modiano, Lizhong Zhang, “ Cooperative Routing in Wireless Networks ,”  Allerton Conference on Communications, Control and Computing, October, 2003, pgs. 1270–1279.**

61.   Poompat Saengudomlert, Eytan Modiano and Robert Gallager, “ Dynamic Wavelength Assignment for WDM all optical Tree Networks ,”  Allerton Conference on Communications, Control and Computing, October, 2003, 915–924.**

60.   Aradhana Narula-Tam and Eytan Modiano, “ Designing Physical Topologies that Enable Survivable Routing of Logical Rings ,”  IEEE Workshop on Design of Reliable Communication Networks (DRCN), October, 2003, pgs. 379–386.

59.   Anand Srinivas and Eytan Modiano, “ Minimum Energy Disjoint Path Routing in Wireless Ad Hoc Networks ,”  ACM Mobicom, San Diego, Ca, September, 2003, pgs. 122–133.**

58.   Michael Neely and Eytan Modiano, “ Improving Delay in Ad-Hoc Mobile Networks Via Redundant Packet Transfers ,”  Conference on Information Science and System, Baltimore, MD, March, 2003 (6 pages; CD proceedings – page numbers not available).**

57.   Michael Neely, Eytan Modiano and Charles Rohrs, “ Dynamic Power Allocation and Routing for Time Varying Wireless Networks ,”  IEEE Infocom 2003, San Francisco, CA, April, 2003, pgs. 745–755.**

56.   Alvin Fu, Eytan Modiano, and John Tsitsiklis, “ Optimal Energy Allocation for Delay-Constrained Data Transmission over a Time-Varying Channel ,”  IEEE Infocom 2003, San Francisco, CA, April, 2003, pgs. 1095–1105.**

55.   Poompat Saengudomlert, Eytan Modiano and Rober Gallager, “ On-line Routing and Wavelength Assignment for Dynamic Traffic in WDM Ring and Torus Networks ,”  IEEE Infocom 2003, San Francisco, CA, April, 2003, pgs. 1805–1815.**

54.   Li-Wei Chen and Eytan Modiano, “ Efficient Routing and Wavelength Assignment for Reconfigurable WDM Networks with Wavelength Converters ,”  IEEE Infocom 2003, San Francisco, CA, April, 2003, pgs. 1785–1794. Selected as one of the best papers of Infocom 2003 for fast track publication in IEEE/ACM Transactions on Networking.**

53.   Mike Neely, Jun Sun and Eytan Modiano, “ Delay and Complexity Tradeoffs for Dynamic Routing and Power Allocation in a Wireless Network ,”  Allerton Conference on Communication, Control, and Computing, Allerton, Illinois, October, 2002, pgs. 157 –159.**

52.   Anand Ganti, Eytan Modiano and John Tsitsiklis, “ Transmission Scheduling for Multi-Channel Satellite and Wireless Networks ,”  Allerton Conference on Communication, Control, and Computing, Allerton, Illinois, October, 2002, pgs. 1318–1327.**

51.   Poompat Saengudomlert, Eytan Modiano, and Robert G. Gallager, “ Optimal Wavelength Assignment for Uniform All-to-All Traffic in WDM Tree Networks ,”  Allerton Conference on Communication, Control, and Computing, Allerton, Illinois, October, 2002, pgs. 528–537.**

50.   Hungjen Wang, Eytan Modiano and Muriel Medard, “ Partial Path Protection for WDM Networks: End-to-End Recovery Using Local Failure Information ,”  IEEE International Symposium on Computer Communications (ISCC), Taormina, Italy, July 2002, pgs. 719–725.**

49.   Jun Sun and Eytan Modiano, “ Capacity Provisioning and Failure Recovery in Mesh-Torus Networks with Application to Satellite Constellations ,”  IEEE International Symposium on Computer Communications (ISCC), Taormina, Italy, July 2002, pgs. 77–84.**

48.   Alvin Fu, Eytan Modiano, and John Tsitsiklis, “ Optimal Energy Allocation and Admission Control for Communications Satellites ,”  IEEE INFOCOM 2002, New York, June, 2002, pgs. 648–656.**

47.   Michael Neely, Eytan Modiano and Charles Rohrs, “ Power and Server Allocation in a Multi-Beam Satellite with Time Varying Channels ,”  IEEE INFOCOM 2002, New York, June, 2002, pgs. 1451–1460..**

46.   Mike Neely, Eytan Modiano and Charles Rohrs, “ Tradeoffs in Delay Guarantees and Computation Complexity for N x N Packet Switches ,”  Conference on Information Science and Systems, Princeton, NJ, March, 2002, pgs. 136–148.**

45.   Alvin Fu, Eytan Modiano and John Tsitsiklis, “ Transmission Scheduling Over a Fading Channel with Energy and Deadline Constraints ,”  Conference on Information Science and System, Princeton, NJ, March, 2002, pgs. 1018–1023.**

44.   Chunmei Liu and Eytan Modiano, “ On the Interaction of Layered Protocols: The Case of Window Flow Control and ARQ ,”  Conference on Information Science and System, Princeton, NJ, March, 2002, pgs. 118–124.**

43.   Mike Neely, Eytan Modiano and Charles Rohrs, “ Packet Routing over Parallel Time-varying Queues with Application to Satellite and Wireless Networks ,”  Conference on Information Science and System, Princeton, NJ, March, 2002, pgs. 360–366.**

42.   Ahluwalia Ashwinder, Eytan Modiano and Li Shu, “ On the Complexity and Distributed Construction of Energy Efficient Broadcast Trees in Static Ad Hoc Wireless Networks ,”  Conference on Information Science and System, Princeton, NJ, March, 2002, pgs. 807–813.**

41.   Jun Sun and Eytan Modiano, “ Capacity Provisioning and Failure Recovery for Satellite Constellations ,”  Conference on Information Science and System, Princeton, NJ, March, 2002, pgs. 1039–1045.**

40.   Eytan Modiano, Hungjen Wang, and Muriel Medard, “ Partial Path Protection for WDM networks ,”  Informs Telecommunications Conference, Boca Raton, FL, March 2002, pgs. 78–79.**

39.   Poompat Saengudomlert, Eytan H. Modiano, and Robert G. Gallager, “ An On-Line Routing and Wavelength Assignment Algorithm for Dynamic Traffic in a WDM Bidirectional Ring ,”  Joint Conference on Information Sciences (JCIS), Durham, North Carolina, March, 2002, pgs. 1331–1334.**

38.   Randy Berry and Eytan Modiano, “ Switching and Traffic Grooming in WDM Networks ,”  Joint Conference on Information Sciences (JCIS), Durham, North Carolina, March, 2002, pgs. 1340–1343.

37.   Eytan Modiano, Hungjen Wang, and Muriel Medard, “ Using Local Information for WDM Network Protection ,”  Joint Conference on Information Sciences (JCIS), Durham, North Carolina, March, 2002, pgs. 1398–1401.**

36.   Aradhana Narula-Tam and Eytan Modiano, “ Network architectures for supporting survivable WDM rings ,”  IEEE/OSA Optical Fiber Conference (OFC) 2002, Anaheim, CA, March, 2002, pgs. 105–107.

35.   Michael Neely, Eytan Modiano, Charles Rohrs, “ Packet Routing over Parallel Time-Varying Queues with Application to Satellite and Wireless Networks ,”  Allerton Conference on Communication, Control, and Computing, Allerton, Illinois, September, 2001, pgs. 1110-1111.**

34.   Eytan Modiano and Randy Berry, “ The Role of Switching in Reducing Network Port Counts ,”  Allerton Conference on Communication, Control, and Computing, Allerton, Illinois, September, 2001, pgs. 376-385.

33.   Eytan Modiano, “ Resource allocation and congestion control in next generation satellite networks ,”  IEEE Gigabit Networking Workshop (GBN 2001), Anchorage, AK, April 2001, (2 page summary-online proceedings).

32.   Eytan Modiano and Aradhana Narula-Tam, “ Survivable Routing of Logical Topologies in WDM Networks ,”  IEEE Infocom 2001, Anchorage, AK, April 2001, pgs. 348–357.

31.   Michael Neely and Eytan Modiano, “ Convexity and Optimal Load Distribution in Work Conserving */*/1 Queues ,”  IEEE Infocom 2001, Anchorage, AK, April 2001, pgs. 1055–1064.

30.   Eytan Modiano and Randy Berry, “ Using Grooming Cross- Connects to Reduce ADM Costs in Sonet/WDM Ring Networks ,”  IEEE/OSA Optical Fiber Conference (OFC) 2001, Anaheim, CA March 2001, pgs. WL1- WL3.

29.   Eytan Modiano and Aradhana Narula-Tam, “ Designing Survivable Networks Using Effective Rounting and Wavelenght Assignment (RWA) ,”  IEEE/OSA Optical Fiber Conference (OFC) 2001, Anaheim, CA March 2001, pgs. TUG5-1 – TUG5– 3.

28.   Roop Ganguly and Eytan Modiano, “ Distributed Algorithms and Architectures for Optical Flow Switching in WDM networks ,”  IEEE International Symposium on Computer Communications (ISCC 2000), Antibes, France, July 2000, pgs. 134–139.

27.   Aradhana Narula-Tam, Philip J. Lin and Eytan Modiano, “ Wavelength Requirements for Virtual topology Reconfiguration in WDM Ring Networks ,”  IEEE International Conference on Communications (ICC 2000), New Orleans, LA, June 2000, pgs. 1650–1654.

26.   Eytan Modiano, “Optical Flow Switching for the Next Generation Internet,”  IEEE Gigabit Networking Workshop (GBN 2000), Tel-aviv, March 2000 (2 page summary-online proceedings).

25.   Aradhana Narula and Eytan Modiano, “ Dynamic Reconfiguration in WDM Packet Networks with Wavelength Limitations ,”  IEEE/OSA Optical Fiber Conference (OFC) 2000, Baltimore, MD, March, 2000, pgs. 1210–1212.

24.   Brett Schein and Eytan Modiano, “ Quantifying the benefits of configurability in circuit-switched WDM ring networks ,”  IEEE Infocom 2000, Tel Aviv, Israel, April, 2000, pgs.1752–1760..***

23.   Aradhana Narula-Tam and Eytan Modiano, “ Load Balancing Algorithms for WDM-based IP networks ,”  IEEE Infocom 2000, Tel Aviv, Israel, April, 2000, pgs. 1010–1019.

22.   Nan Froberg, M. Kuznetsov, E. Modiano, et. al., “ The NGI ONRAMP test bed: Regional Access WDM technology for the Next Generation Internet ,”  IEEE LEOS ’99, October, 1999, pgs. 230–231.

21.   Randy Berry and Eytan Modiano, “ Minimizing Electronic Multiplexing Costs for Dynamic Traffic in Unidirectional SONET Ring Networks ,”  IEEE International Conference on Communications (ICC ’99), Vancouver, CA, June 1999, pgs. 1724–1730..***

20.   Brett Schein and Eytan Modiano, “Increasing Traffic Capacity in WDM Ring Networks via Topology Reconfiguration,”  Conference on Information Science and Systems, Baltimore, MD, March 1999, pgs. 201 – 206.

19.   Eytan Modiano and Richard Barry, “ Design and Analysis of an Asynchronous WDM Local Area Network Using a Master/Slave Scheduler ,”  IEEE Infocom ’99, New York, NY, March 1999, pgs. 900–907.

18.   Randy Berry and Eytan Modiano, “ Grooming Dynamic Traffic in Unidirectional SONET Ring Networks ,”  IEEE/OSA Optical Fiber Conference (OFC) ’99, San Diego, CA, February 1999, pgs. 71–73.

17.   Angela Chiu and Eytan Modiano, “ Reducing Electronic Multiplexing Costs in Unidirectional SONET/WDM Ring Networks Via Efficient Traffic Grooming ,”  IEEE Globecom ’98, Sydney, Australia, November 1998, pgs. 322–327.

16.   Eytan Modiano, “ Throughput Analysis of Unscheduled Multicast Transmissions in WDM Broadcast-and-Select Networks ,”  IEEE International Symposium on Information Theory, Boston, MA, September 1998, pg. 167.

15.   Eytan Modiano and Angela Chiu, “Traffic Grooming Algorithms for Minimizing Electronic Multiplexing Costs in Unidirectional SONET/WDM Ring Networks,”  Conference on Information Science and Systems, Princeton, NJ, March 1998, 653–658.

14.   Eytan Modiano and Eric Swanson, “ An Architecture for Broadband Internet Services over a WDM-based Optical Access Network ,”  IEEE Gigabit Networking Workshop (GBN ’98), San Francisco, CA, March 1998 (2 page summary-online proceedings).

13.   Eytan Modiano, “ Unscheduled Multicasts in WDM Broadcast-and-Select Networks ,”  IEEE Infocom ’98, San Francisco, CA, March 1998, pgs. 86–93.

12.   Eytan Modiano, Richard Barry and Eric Swanson, “ A Novel Architecture and Medium Access Control (MAC) protocol for WDM Networks ,”  IEEE/OSA Optical Fiber Conference (OFC) ’98, San Jose, CA, February 1998, pgs. 90–91.

11.   Eytan Modiano, “ Scheduling Algorithms for Message Transmission Over a Satellite Broadcast System ,”  IEEE MILCOM 97, Monterey, CA, November 1997, pgs. 628–634.

10.   Eytan Modiano, “ Scheduling Packet Transmissions in A Multi-hop Packet Switched Network Based on Message Length ,”  IEEE International Conference on Computer Communications and Networks (IC3N) Las Vegas, Nevada, September 1997, pgs. 350–357.

9.   Eytan Modiano, “A Simple Algorithm for Optimizing the Packet Size Used in ARQ Protocols Based on Retransmission History,”  Conference on Information Science and Systems, Baltimore, MD, March 1997, pgs. 672–677.

8.   Eytan Modiano, “ A Multi-Channel Random Access Protocol for the CDMA Channel ,”  IEEE PIMRC ’95, Toronto, Canada, September 1995, pgs. 799–803.

7.   Eytan Modiano Jeffrey Wieselthier and Anthony Ephremides, “ A Simple Derivation of Queueing Delay in a Tree Network of Discrete-Time Queues with Deterministic Service Times ,”  IEEE International Symposium on Information Theory, Trondheim, Norway, June 1994, pg. 372.

6.   Eytan Modiano, Jeffrey Wieselthier and Anthony Ephremides, “An Approach for the Analysis of Packet Delay in an Integrated Mobile Radio Network,”  Conference on Information Sciences and Systems, Baltimore, MD, March 1993, pgs. 138-139.

5.   Eytan Modiano and Anthony Ephremides, “ A Method for Delay Analysis of Interacting Queues in Multiple Access Systems ,”  IEEE INFOCOM 1993, San Francisco, CA, March 1993, pgs. 447 – 454.

4.   Eytan Modiano and Anthony Ephremides, “ A Model for the Approximation of Interacting Queues that Arise in Multiple Access Schemes ,”  IEEE International Symposium on Information Theory, San Antonio, TX, January 1993, pg. 324.

3.   Eytan Modiano and Anthony Ephremides, “ Efficient Routing Schemes for Multiple Broadcasts in a Mesh ,”  Conference on Information Sciences and Systems, Princeton, NJ, March 1992, pgs. 929 – 934.

2.   Eytan Modiano and Anthony Ephremides, “ On the Secrecy Complexity of Computing a Binary Function of Non-uniformly Distributed Random Variables ,”  IEEE International Symposium on Information Theory, Budapest, Hungary, June 1991, pg. 213.

1.   Eytan Modiano and Anthony Ephremides, “Communication Complexity of Secure Distributed Computation in the Presence of Noise,”  IEEE International Symposium on Information Theory, San Diego, CA, January 1990, pg. 142.

Book Chapters

• Hyang-Won Lee, Kayi Lee, Eytan Modiano, “ Cross-Layer Survivability ” in Cross-Layer Design in Optical Networks, Springer, 2013.
• Li-Wei Chen and Eytan Modiano, “ Geometric Capacity Provisioning for Wavelength-Switched WDM Networks ,” Chapter in Computer Communications and Networks Series: Algorithms for Next Generation Networks, Springer, 2010.
• Amir Khandani, Eytan Modiano, Lizhong Zhang, Jinane Aboundi, “ Cooperative Routing in Wireless Networks ,” Chapter in Advances in Pervasive Computing and Networking, Kluwer Academic Publishers, 2005.
• Jian-Qiang Hu and Eytan Modiano, “ Traffic Grooming in WDM Networks ,” Chapter in Emerging Optical Network Technologies, Kluwer Academic Publishers, to appear, 2004.
• Eytan Modiano, “ WDM Optical Networks ,” Wiley Encyclopedia of Telecommunications (John Proakis, Editor), 2003.
• Eytan Modiano, “ Optical Access Networks for the Next Generation Internet ,” in Optical WDM Networks: Principles and Practice, Kluwer Academic Prublishers, 2002.
• Eytan Modiano, Richard Barry and Eric Swanson, “ A Novel Architecture and Medium Access Control protocol for WDM Networks ,” Trends in Optics and Photonics Series (TOPS) volume on Optical Networks and Their Applications, 1998.
• Eytan Modiano and Kai-Yeung Siu, “Network Flow and Congestion Control,” Wiley Encyclopedia of Electrical and Electronics Engineering, 1999.

Technical Reports

• Amir Khandani, Eytan Modiano, Jinane Abounadi, Lizhong Zheng, “Reliability and Route Diversity in Wireless Networks, ” MIT LIDS Technical Report number 2634, November, 2004.
• Anand Srinivas and Eytan Modiano, “Minimum Energy Disjoint Path Routing in Wireless Ad Hoc Networks, ” MIT LIDS Technical Report, P-2559, March, 2003.
• Eytan Modiano and Aradhana Narula-Tam, “Survivable lightpath routing: a new approach to the design of WDM-based networks, ” LIDS report 2552, October, 2002.
• Michael Neely, Eytan Modiano and Charles Rohrs, “Packet Routing over Parallel Time-Varying Queues with Application to Satellite and Wireless Networks,” LIDS report 2520, September, 2001.
• Jun Sun and Eytan Modiano, “Capacity Provisioning and Failure Recovery in Mesh-Torus Networks with Application to Satellite Constellations,” LIDS report 2518, September, 2001.
• Hungjen Wang, Eytan Modiano and Muriel Medard, “Partial Path Protection for WDM Networks: End-to-End Recovery Using Local Failure Information, ” LIDS report 2517, Sept. 2001.
• Alvin Fu, Eytan Modiano, and John Tsitsiklis, “Optimal Energy Allocation and Admission Control for Communications Satellites, ” LIDS report 2516, September, 2001.
• Michael Neely, Eytan Modiano and Charles Rohrs, “Power and Server Allocation in a Multi-Beam Satellite with Time Varying Channels, ” LIDS report 2515, September, 2001.
• Eytan Modiano, “Scheduling Algorithms for Message Transmission Over the GBS Satellite Broadcast System, ” Lincoln Laboratory Technical Report Number TR-1035, June 1997.
• Eytan Modiano, “Scheduling Packet Transmissions in A Multi-hop Packet Switched Network Based on Message Length, ” Lincoln Laboratory Technical Report number TR-1036, June, 1997.

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Career Development International

ISSN : 1362-0436

Article publication date: 6 May 2014

The purpose of this paper is to review and synthesize research and theory on the definition, antecedents, outcomes, and mechanisms of networking in organizations.

Design/methodology/approach

Descriptions of networking are reviewed and an integrated definition of networking in organizations is presented. Approaches for measuring and studying networking are considered and the similarities and differences of networking with related constructs are discussed. A theoretical model of the antecedents and outcomes of networking is presented with the goal of integrating existing networking research. Mechanisms through which networking leads to individual and organizational outcomes are also considered.

Networking is defined as goal-directed behavior which occurs both inside and outside of an organization, focussed on creating, cultivating, and utilizing interpersonal relationships. The current model proposes that networking is influenced by a variety of individual, job, and organizational level factors and leads to increased visibility and power, job performance, organizational access to strategic information, and career success. Access to information and social capital are proposed as mechanisms that facilitate the effects of networking on outcomes.

Originality/value

Networking is held to be of great professional value for ambitious individuals and organizations. However, much of the research on networking has been spread across various disciplines. Consequentially, consensus on many important topics regarding networking remains notably elusive. This paper reviews and integrates existing research on networking in organizations and proposes directions for future study. A comprehensive definition and model of networking is presented and suggestions to researchers are provided.

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Gibson, C. , H. Hardy III, J. and Ronald Buckley, M. (2014), "Understanding the role of networking in organizations", Career Development International , Vol. 19 No. 2, pp. 146-161. https://doi.org/10.1108/CDI-09-2013-0111

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Topics in Networking Research

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• Debasis Mitra 1  

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 4516))

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What are the big movements in networking that researchers should heed? A standout is the global spread of communities of interest (the networking analogue of the flat world) and their need for “dynamic virtual networks” that support rich applications requiring resources from several domains. The imperative for inter-networking, i.e., the enablement of coordinated sharing of resources across multiple domains, is certain. This challenge has many facets, ranging from the organizational, e.g., different, possibly competing, owners to the technical, e.g., different technologies. Yet another key characteristic of the emerging networking environment is that the service provider is required to handle ever-increasing uncertainty in demand, both in volume and time. On the other hand there are new instruments available to handle the challenge. Thus, inter-networking and uncertainty management are important challenges of emerging networking that deserve attention from the research community.

We describe research that touch on both topics. First, we consider a model of data-optical inter-networking, where routes connecting end-points in data domains are concatenation of segments in the data and optical domains. The optical domain in effect acts as a carrier’s carrier for multiple data domains. The challenge to inter-networking stems from the limited view that the data and optical domains have of each other. Coordination has to be enabled through parsimonious and qualitatively restrictive information exchange across domains. Yet the overall optimization objective, which is to maximize end-to-end carried traffic with minimum lightpath provisioning cost, enmeshes data and optical domains. This example of inter-networking also involves two technologies. A mathematical reflection of the latter fact is the integrality of some of the decision variables due to wavelengths being the bandwidth unit in optical transmission. Through an application of Generalized Bender’s Decomposition the problem of optimizing provisioning and routing is decomposed into sub-problems, which are solved by the different domains and the results exchanged in iterations that provably converge to the global optimum.

In turning to uncertainty management we begin by presenting a framework for stochastic traffic management. Traffic demands are uncertain and given by probability distributions. While there are alternative perspectives (and metrics) to resource usage, such as social welfare and network revenue, we adopt the latter, which is aligned with the service provider’s interests. Uncertainty introduces the risk of misallocation of resources. What is the right measure of risk in networking? We examine various definitions of risk, some taken from modern portfolio theory, and suggest a balanced solution. Next we consider the optimization of an objective which is a risk-adjusted measure of network revenue. We obtain conditions under which the optimization problem is an instance of convex programming. Studies of the properties of the solution show that it asymptotically meets the stochastic efficiency criterion. Service providers’ risk mitigation policies are suggested. For instance, by selecting the appropriate mix of long-term contracts and opportunistic servicing of random demand, the service provider can optimize its risk-adjusted revenue. The “efficient frontier”, which is the set of Pareto optimal pairs of mean revenue and revenue risk, is useful to the service provider in selecting its operating point.

Joint work with Qiong Wang and Anwar Walid, Bell Labs, Murray Hill.

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Mitra, D. (2007). Topics in Networking Research. In: Mason, L., Drwiega, T., Yan, J. (eds) Managing Traffic Performance in Converged Networks. ITC 2007. Lecture Notes in Computer Science, vol 4516. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72990-7_3

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• 15 Latest Networking Research Topics for Students

Comparative analysis between snort and suricata IDS software(s)

Description of the topic

The main focus of this research is to conduct a comparative analysis between Snort and Suricata software to determine which IDS software can provide better performance. There are various IDS software(s) available that can be used by organizations but it is difficult to identify which one is best (Aldarwbi et al., 2022). Different organizational structures are often facing problems while setting up an IDS system which results in false positives and intrusions. Through this research, it can be identified which IDS software is better and what secure configuration is required to detect intrusions (Waleed et al., 2022).

Research objectives

• To evaluate Snort and Suricata IDS software(s) to determine the most optimal one.
• To identify the false positive rate of Snort and Suricata on the networked environment.

Research questions

RQ1: Which IDS software can perform better on the production network in terms of performance, security, scalability and reliability?

RQ2: What different ways can be followed to deal with false positive problems in IDS technology?

Research methodology

The given research objectives and research questions can be addressed using quantitative research methodology where an experimental approach can be followed. For the given topic, both Snort and Suricata IDS systems should be configured and tested against different attacks. Depending on the findings, it can be analyzed which IDS software can perform better in terms of performance and security (Shuai & Li, 2021).

• Aldarwbi, M.Y., Lashkari, A.H. and Ghorbani, A.A. (2022) “The sound of intrusion: A novel network intrusion detection system,” Computers and Electrical Engineering , 104, p. 108455.
• Shuai, L. and Li, S. (2021) “Performance optimization of Snort based on DPDK and Hyperscan,” Procedia Computer Science , 183, pp. 837-843.
• Waleed, A., Jamali, A.F. and Masood, A. (2022) “Which open-source ids? Snort, Suricata or Zeek,” Computer Networks , 213, p. 109116.

Role of honeypots and honey nets in network security

Network Security has become essential nowadays and there is a need for setting up robust mechanisms to maintain confidentiality and integrity (Feng et al., 2023). Due to the number of security mechanisms available, organizations found it hard to finalize and implement them on their network. For example, honey pots and honeynet approaches look almost the same and have the same purpose but work differently. Under this research topic, the configuration of honeynets and honeypots can be done to check which one can perform better security in terms of trapping cyber attackers. The entire implementation can be carried out in the cloud-based instance for improved security and it can be identified which type of honey pot technology must be preferred (Maesschalck et al., 2022).

• To set up a honey pot system using Open Canary on the virtual instance to protect against cyber attackers.
• To set up a honeynet system on the virtual instance to assure protection is provided against malicious attackers.
• To test honeypots and honeynets by executing DDoS attacks to check which can provide better security.

RQ1: Why is there a need for using honeypots over honey pots in a production networked environment?

RQ2: What are the differences between cloud-based and local honey pot systems for endpoint protection?

This research can be carried out using the quantitative method of research. At the initial stage, the implementation of honeypots and honeypots can be done on the virtual instance following different security rules. Once the rules are applied, the testing can be performed using a Kali Linux machine to check whether honey pots were effective or honeynets (Gill et al., 2020).

• Feng, H. et al. (2023) “Game theory in network security for Digital Twins in industry,” Digital Communications and Networks [Preprint].
• Gill, K.S., Saxena, S. and Sharma, A. (2020) “GTM-CSEC: A game theoretic model for cloud security based on ids and Honeypot,” Computers & Security , 92, p. 101732
• Maesschalck, S. et al. (2022) “Don’t get stung, cover your ICS in honey: How do honeypots fit within industrial control system security,” Computers & Security , 114, p. 102598.

How do malware variants are progressively improving?

This research can be based on evaluating how malware variants are progressively improving and what should be its state in the coming future. Malware is able to compromise confidential user’s information assets which is why this research can be based on identifying current and future consequences owing to its improvements (Deng et al., 2023). In this field, there is no research work that has been carried out to identify how malware variants are improving their working and what is expected to see in future. Once the evaluation is done, a clear analysis can also be done on some intelligent preventive measures to deal with dangerous malware variants and prevent any kind of technological exploitation (Tang et al., 2023).

• To investigate types of malware variants available to learn more about malware's hidden features.
• To focus on future implications of malware executable programs and how they can be avoided.
• To discuss intelligent solutions to deal with all malware variants.

RQ1: How do improvements in malware variants impact enterprises?

RQ2: What additional solutions are required to deal with malware variants?

In this research, qualitative analysis can be conducted on malware variants and the main reason behind their increasing severity. The entire research can be completed based on qualitative research methodology to answer defined research questions and objectives. Some real-life case studies should also be integrated into the research which can be supported by the selected topic (Saidia Fasci et al., 2023).

• Deng, H. et al. (2023) “MCTVD: A malware classification method based on three-channel visualization and deep learning,” Computers & Security , 126, p. 103084.
• Saidia Fasci, L. et al. (2023) “Disarming visualization-based approaches in malware detection systems,” Computers & Security , 126, p. 103062.
• Tang, Y. et al. (2023) “BHMDC: A byte and hex n-gram based malware detection and classification method,” Computers & Security , p. 103118.

Implementation of IoT - enabled smart office/home using cisco packet tracer

The Internet of Things has gained much more attention over the past few years which is why each enterprise and individual aims at setting up an IoT network to automate their processes (Barriga et al., 2023). This research can be based on designing and implementing an IoT-enabled smart home/office network using Cisco Packet Tracer software. Logical workspace, all network devices, including IoT devices can be used for preparing a logical network star topology (Elias & Ali, 2014). To achieve automation, the use of different IoT rules can be done to allow devices to work based on defined rules.

• To set up an IoT network on a logical workspace using Cisco Packet Tracer simulation software.
• To set up IoT-enabled rules on an IoT registration server to achieve automation (Hou et al., 2023).

RQ: Why is the Cisco packet tracer preferred for network simulation over other network simulators?

At the beginning of this research, a quantitative research methodology can be followed where proper experimental set-up can be done. As a packet tracer is to be used, the star topology can be used to interconnect IoT devices, sensors and other network devices at the home/office. Once a placement is done, the configuration should be done using optimal settings and all IoT devices can be connected to the registration server. This server will have IoT rules which can help in achieving automation by automatically turning off lights and fans when no motion is detected (Baggan et al., 2022).

• Baggan, V. et al. (2022) “A comprehensive analysis and experimental evaluation of Routing Information Protocol: An Elucidation,” Materials Today: Proceedings , 49, pp. 3040–3045.
• Barriga, J.A. et al. (2023) “Design, code generation and simulation of IOT environments with mobility devices by using model-driven development: Simulateiot-Mobile,” Pervasive and Mobile Computing , 89, p. 101751.
• Elias, M.S. and Ali, A.Z. (2014) “Survey on the challenges faced by the lecturers in using packet tracer simulation in computer networking course,” Procedia - Social and Behavioral Sciences , 131, pp. 11–15.
• Hou, L. et al. (2023) “Block-HRG: Block-based differentially private IOT networks release,” Ad Hoc Networks , 140, p. 103059.

Comparative analysis between AODV, DSDV and DSR routing protocols in WSN networks

For wireless sensor networks (WSN), there is a major need for using WSN routing rather than performing normal routines. As WSN networks are self-configured, there is a need for an optimal routing protocol that can improve network performance in terms of latency, jitter, and packet loss (Luo et al., 2023). There are often various problems faced when WSN networks are set up due to a lack of proper routing protocol selection. As a result of this, severe downtime is faced and all links are not able to communicate with each other easily (Hemanand et al., 2023). In this research topic, the three most widely used WSN routing protocols AODV, DSDV and DSR can be compared based on network performance. To perform analysis, three different scenarios can be created in network simulator 2 (Ns2).

• To create three different scenarios on ns2 software to simulate a network for 1 to 100 seconds.
• To analyze which WSN routing is optimal in terms of network performance metrics, including latency, jitter and packet loss.
• To use CBR and NULL agents for all wireless scenarios to start with simulation purposes.

RQ: How do AODV, DSR and DSDV routing protocols differ from each other in terms of network performance?

This research can be carried out using a quantitative research method. The implementation for the provided research topic can be based on Ns2 simulation software where three different scenarios can be created (AODV, DSDV and DSR). For each scenario, NULL, CSR and UDP agents can be done to start with simulation for almost 1 to 100 seconds. For all transmissions made during the given time, network performance can be checked to determine which routing is best (Mohapatra & Kanungo, 2012).

• Human and, D. et al. (2023) “Analysis of power optimization and enhanced routing protocols for Wireless Sensor Networks,” Measurement: Sensors , 25, p. 100610. Available at: https://doi.org/10.1016/j.measen.2022.100610.
• Luo, S., Lai, Y. and Liu, J. (2023) “Selective forwarding attack detection and network recovery mechanism based on cloud-edge cooperation in software-defined wireless sensor network,” Computers & Security , 126, p. 103083. Available at: https://doi.org/10.1016/j.cose.2022.103083.
• Mohapatra, S. and Kanungo, P. (2012) “Performance analysis of AODV, DSR, OLSR and DSDV routing protocols using NS2 Simulator,” Procedia Engineering , 30, pp. 69–76. Available at: https://doi.org/10.1016/j.proeng.2012.01.835.

Securing wireless network using AAA authentication and WLAN controller

Wireless networks often face intrusion attempts due to insecure protocols and sometimes open SSIDs. As a result of this, man-in-the-middle and eavesdropping attacks become easier which results in the loss of confidential information assets (Sivasankari & Kamalakkannan, 2022). When it comes to managing networks in a large area, there are higher chances for attacks that enable cyber attackers in intercepting ongoing communication sessions. However, there is currently no research conducted where the use of AAA authentication has been done with WLAN controllers to make sure a higher level of protection is provided (Nashwan, 2021). The proposed research topic can be based on securing wireless networks with the help of AAA authentication and WLAN controllers. The use of AAA authentication can be done to set up a login portal for users whilst the WLAN controller can be used for managing all wireless access points connected to the network (Nashwan, 2021).

• To set up AAA authentication service on the wireless network simulated on Cisco Packet Tracer for proper access control.
• To set up a WLAN controller on the network to manage all wireless access points effortlessly.
• To use WPA2-PSK protocol on the network to assure guest users are only able to access wireless networks over a secure protocol.

RQ1: What additional benefits are offered by AAA authentication on the WLAN networks?

RQ2: Why are wireless networks more likely to face network intrusions than wired networks?

This research topic is based on the secure implementation of a wireless LAN network using a Cisco packet tracer. Hence, this research can be carried out using a quantitative research method. The implementation can be carried out using AAA authentication which can assure that access control is applied for wireless logins. On the other hand, a WLAN controller can also be configured which can ensure that all WAPs are managed (ZHANG et al., 2012).

• Nashwan, S. (2021) “AAA-WSN: Anonymous Access Authentication Scheme for wireless sensor networks in Big Data Environment,” Egyptian Informatics Journal , 22(1), pp. 15–26.
• Sivasankari, N. and Kamalakkannan, S. (2022) “Detection and prevention of man-in-the-middle attack in IOT network using regression modeling,” Advances in Engineering Software , 169, p. 103126.
• ZHANG, J. et al. (2012) “AAA authentication for Network mobility,” The Journal of China Universities of Posts and Telecommunications , 19(2), pp. 81-86.

OWASP's approach to secure web applications from web application exploits

The research can revolve around the development of web applications by considering OWASP's top 10 rules. Usually, web applications are deployed by organizations depending on their requirements and these applications are vulnerable to various exploits, including injection, broken authentication and other forgery attacks (Poston, 2020). Identifying every single vulnerability is difficult when reference is not taken and often organizations end up hosting a vulnerable server that leads to privacy issues and compromises confidential information easily. In this research, OWASP's top 10 approaches can be followed to develop a secure web application that can be able to protect against top web application exploits. This approach is based on emphasizing severe and minor vulnerabilities which must be patched for protecting against web application attacks (Deepa & Thilagam, 2016).

• The first objective can be setting up an insecure web application on the cloud environment which can be exploited with different techniques.
• The second objective can be to consider all techniques and procedures provided by OWASP's top 10 methodologies.
• The last objective can be applying all fixes to insecure web applications to make them resistant to OWASP top 10 attacks (Sonmez, 2019).

RQ1: What are the benefits of using OWASP's top 10 approaches to harden web applications in comparison to other security approaches?

The research methodology considered for this research project can be quantitative using an experimental approach. The practical work can be done for the selected topic using AWS or the Azure cloud platform. Simply, a virtual web server can be configured and set up with a secure and insecure web application. Following OWASP's top 10 techniques and procedures, the web application can be secured from possible attacks. In addition, insecure applications can also be exploited and results can be evaluated (Applebaum et al., 2021).

• Applebaum, S., Gaber, T. and Ahmed, A. (2021) “Signature-based and machine-learning-based web application firewalls: A short survey,” Procedia Computer Science , 189, pp. 359–367. Available at: https://doi.org/10.1016/j.procs.2021.05.105.
• Deepa, G. and Thilagam, P.S. (2016) “Securing web applications from injection and logic vulnerabilities: Approaches and challenges,” Information and Software Technology , 74, pp. 160–180. Available at: https://doi.org/10.1016/j.infsof.2016.02.005.
• Poston, H. (2020) “Mapping the owasp top Ten to the blockchain,” Procedia Computer Science , 177, pp. 613-617. Available at: https://doi.org/10.1016/j.procs.2020.10.087.
• Sonmez, F.Ö. (2019) “Security qualitative metrics for Open Web Application Security Project Compliance,” Procedia Computer Science , 151, pp. 998-1003. Available at: https://doi.org/10.1016/j.procs.2019.04.140.

Importance of configuring RADIUS (AAA) server on the network

User authentication has become significant nowadays as it guarantees that a legitimate user is accessing the network. But a problem is faced when a particular security control is to be identified for authentication and authorization. These controls can be categorized based on mandatory access controls, role-based access control, setting up captive portals and many more. Despite several other security controls, one of the most efficient ones is the RADIUS server (SONG et al., 2008). This server can authenticate users on the network to make sure network resources are accessible to only legal users. This research topic can be based on understanding the importance of RADIUS servers on the network which can also be demonstrated with the help of the Cisco Packet Tracer. A network can be designed and equipped with a RADIUS server to ensure only legal users can access network resources (WANG et al., 2009).

• To configure RADIUS (AAA) server on the network which can be able to authenticate users who try to access network resources.
• To simulate a network on a packet tracer simulation software and verify network connectivity.

RQ1: What are other alternatives to RADIUS (AAA) authentication servers for network security?

RQ2: What are the common and similarities between RADIUS and TACACS+ servers?

As a logical network is to be designed and configured, a quantitative research methodology can be followed. In this research coursework, a secure network design can be done using a packet tracer network simulator, including a RADIUS server along with the DMZ area. The configuration for the RADIUS server can be done to allow users to only access network resources by authenticating and authorizing (Nugroho et al., 2022).

• Nugroho, Y.S. et al. (2022) “Dataset of network simulator related-question posts in stack overflow,” Data in Brief , 41, p. 107942.
• SONG, M., WANG, L. and SONG, J.-de (2008) “A secure fast handover scheme based on AAA protocol in Mobile IPv6 Networks,” The Journal of China Universities of Posts and Telecommunications , 15, pp. 14-18.
• WANG, L. et al. (2009) “A novel congestion control model for interworking AAA in heterogeneous networks,” The Journal of China Universities of Posts and Telecommunications , 16, pp. 97-101.

Comparing mod security and pF sense firewall to block illegitimate traffic

Firewalls are primarily used for endpoint security due to their advanced features ranging from blocking to IDS capabilities and many more. It is sometimes challenging to identify which type of firewall is best and due to this reason, agencies end up setting up misconfigured firewalls (Tiwari et al., 2022). This further results in a cyber breach, destroying all business operations. The research can be emphasizing conducting a comparison between the two most widely used firewalls i.e. Mod Security and pF sense. Using a virtualized environment, both firewalls can be configured and tested concerning possible cyber-attacks (Lu & Yang, 2020).

• To use the local environment to set up Mod security and pF sense firewall with appropriate access control rules.
• To test both firewalls by executing distributed denial of service attacks from a remote location.
• To compare which type of firewall can provide improved performance and robust security.

RQ: How do Mod security and pF sense differ from each other in terms of features and performance?

The practical experimentation for both firewalls can be done using a virtualized environment where two different machines can be created. Hence, this research can be carried out using a quantitative research method . The first machine can have Mod security and the second machine can have pF sense configured. A new subnet can be created which can have these two machines. The third machine can be an attacking machine which can be used for testing firewalls. The results obtained can be then evaluated to identify which firewall is best for providing security (Uçtu et al., 2021).

• Lu, N. and Yang, Y. (2020) “Application of evolutionary algorithm in performance optimization of Embedded Network Firewall,” Microprocessors and Microsystems , 76, p. 103087.
• Tiwari, A., Papini, S. and Hemamalini, V. (2022) “An enhanced optimization of parallel firewalls filtering rules for scalable high-speed networks,” Materials Today: Proceedings , 62, pp. 4800-4805.
• Uçtu, G. et al. (2021) “A suggested testbed to evaluate multicast network and threat prevention performance of Next Generation Firewalls,” Future Generation Computer Systems , 124, pp. 56-67.

Conducting a comprehensive investigation on the PETYA malware

The main purpose of this research is to conduct a comprehensive investigation of the PETYA malware variant (McIntosh et al., 2021). PETYA often falls under the category of ransomware attacks which not only corrupt and encrypt files but can compromise confidential information easily. Along with PETYA, there are other variants also which lead to a security outage and organizations are not able to detect these variants due to a lack of proper detection capabilities (Singh & Singh, 2021). In this research, a comprehensive analysis has been done on PETYA malware to identify its working and severity level. Depending upon possible causes of infection of PETYA malware, some proactive techniques can also be discussed (Singh & Singh, 2021). A separation discussion can also be made on other malware variants, their features, and many more.

• The main objective of this research is to scrutinize the working of PETYA malware because a ransomware attack can impact the micro and macro environment of the organizations severely.
• The working of PETYA malware along with its source code can be reviewed to identify its structure and encryption type.
• To list all possible CVE IDs which are exploited by the PETYA malware.

RQ1: How dangerous is PETYA malware in comparison to other ransomware malware?

This research can be based on qualitative research methodology to evaluate the working of PETYA malware from various aspects, the methodology followed and what are its implications. The research can be initiated by evaluating the working of PETYA malware, how it is triggered, what encryption is applied and other factors. A sample source code can also be analyzed to learn more about how cryptography is used with ransomware (Abijah Roseline & Geetha, 2021).

• Abijah Roseline, S. and Geetha, S. (2021) “A comprehensive survey of tools and techniques mitigating computer and mobile malware attacks,” Computers & Electrical Engineering , 92, p. 107143.
• McIntosh, T. et al. (2021) “Enforcing situation-aware access control to build malware-resilient file systems,” Future Generation Computer Systems , 115, pp. 568-582.
• Singh, J. and Singh, J. (2021) “A survey on machine learning-based malware detection in executable files,” Journal of Systems Architecture , 112, p. 101861.

Setting up a Live streaming server on the cloud platform

Nowadays, various organizations require a live streaming server to stream content depending upon their business. However, due to a lack of proper hardware, organizations are likely to face high network congestion, slowness and other problems (Ji et al., 2023). Referring to the recent cases, it has been observed that setting up a streaming server on the local environment is not expected to perform better than a cloud-based streaming server configuration (Martins et al., 2019). This particular research topic can be based on setting up a live streaming server on the AWS or Azure cloud platform to make sure high network bandwidth is provided with decreased latency. The research gap analysis would be conducted to analyze the performance of live streaming servers on local and cloud environments in terms of network performance metrics (Bilal et al., 2018).

• To set up a live streaming server on the AWS or Azure cloud platform to provide live streaming services.
• To use load balancers alongside streaming servers to ensure the load is balanced and scalability is achieved.
• To use Wireshark software to test network performance during live streaming.

RQ1: Why are in-house streaming servers not able to provide improved performance in comparison to cloud-based servers?

RQ2: What additional services are provided by cloud service providers which help in maintaining network performance?

The implementation is expected to carry out on the AWS cloud platform with other AWS services i.e. load balancer, private subnet and many more (Efthymiopoulou et al., 2017). Hence, this research can be carried out using a quantitative research method. The configuration of ec2 instances can be done which can act as a streaming server for streaming media and games. For testing this project, the use of OBS studio can be done which can help in checking whether streaming is enabled or not. For network performance, Wireshark can be used for testing network performance (George et al., 2020).

• Bilal, KErbad, A. and Hefeeda, M. (2018) “QoE-aware distributed cloud-based live streaming of multi-sourced Multiview Videos,” Journal of Network and Computer Applications , 120, pp. 130-144.
• Efthymiopoulou, M. et al. (2017) “Robust control in cloud-assisted peer-to-peer live streaming systems,” Pervasive and Mobile Computing , 42, pp. 426-443.
• George, L.C. et al. (2020) “Usage visualization for the AWS services,” Procedia Computer Science , 176, pp. 3710–3717.
• Ji, X. et al. (2023) “Adaptive QoS-aware multipath congestion control for live streaming,” Computer Networks , 220, p. 109470.
• Martins, R. et al. (2019) “Iris: Secure reliable live-streaming with Opportunistic Mobile Edge Cloud offloading,” Future Generation Computer Systems , 101, pp. 272-292.

Significance of using OSINT framework for Network reconnaissance

Network reconnaissance is becoming important day by day when it comes to penetration testing. Almost all white hat hackers are dependent on the OSINT framework to start with network reconnaissance and footprinting when it comes to evaluating organizational infrastructure. On the other hand, cyber attackers are also using this technique to start fetching information about their target. Currently, there is no investigation carried out to identify how effective the OSINT framework is over traditional reconnaissance activities (Liu et al., 2022). This research is focused on using OSINT techniques to analyze victims using different sets of tools like Maltego, email analysis and many other techniques. The analysis can be based on fetching sensitive information about the target which can be used for conducting illegal activities (Abdullah, 2019).

• To use Maltego software to conduct network reconnaissance on the target by fetching sensitive information.
• To compare the OSINT framework with other techniques to analyze why it performs well.

RQ1: What is the significance of using the OSINT framework in conducting network reconnaissance?

RQ2: How can the OSINT framework be used by cyber hackers for conducting illegitimate activities?

The OSINT framework is easily accessible on its official website where different search options are given. Hence, this research can be carried out using a quantitative research method. Depending upon the selected target, each option can be selected and tools can be shortlisted for final implementation. Once the tools are shortlisted, they can be used to conduct network reconnaissance (González-Granadillo et al., 2021). For example, Maltego can be used as it is a powerful software to fetch information about the target.

• Abdullah, S.A. (2019) “Seui-64, bits an IPv6 addressing strategy to mitigate reconnaissance attacks,” Engineering Science and Technology , an International Journal, 22(2), pp. 667–672.
• Gonzalez-Granadillo, G. et al. (2021) “ETIP: An enriched threat intelligence platform for improving OSINT correlation, analysis, visualization and sharing capabilities,” Journal of Information Security and Applications , 58, p. 102715.
• Liu, W. et al. (2022) “A hybrid optimization framework for UAV Reconnaissance Mission Planning,” Computers & Industrial Engineering , 173, p. 108653.

Wired and wireless network hardening in cisco packet tracer

At present, network security has become essential and if enterprises are not paying attention to the security infrastructure, there are several chances for cyber breaches. To overcome all these issues, there is a need for setting up secure wired and wireless networks following different techniques such as filtered ports, firewalls, VLANs and other security mechanisms. For the practical part, the use of packet tracer software can be done to design and implement a highly secure network (Sun, 2022).

• To use packet tracer simulation software to set up secure wired and wireless networks.
• Use different hardening techniques, including access control rules, port filtering, enabling passwords and many more to assure only authorized users can access the network (Zhang et al., 2012).

RQ: Why is there a need for emphasizing wired and wireless network security?

Following the quantitative approach, the proposed research topic implementation can be performed in Cisco Packet Tracer simulation software. Several devices such as routers, switches, firewalls, wireless access points, hosts and workstations can be configured and interconnected using Cat 6 e cabling. For security, every device can be checked and secure design principles can be followed like access control rules, disabled open ports, passwords, encryption and many more (Smith & Hasan, 2020).

• Smith, J.D. and Hasan, M. (2020) “Quantitative approaches for the evaluation of Implementation Research Studies,” Psychiatry Research , 283, p. 112521.
• Sun, J. (2022) “Computer Network Security Technology and prevention strategy analysis,” Procedia Computer Science , 208, pp. 570–576.
• Zhang, YLiang, R. and Ma, H. (2012) “Teaching innovation in computer network course for undergraduate students with a packet tracer,” IERI Procedia , 2, pp. 504–510.

Different Preemptive ways to resist spear phishing attacks

When it comes to social engineering, phishing attacks are rising and are becoming one of the most common ethical issues as it is one of the easiest ways to trick victims into stealing information. This research topic is based on following different proactive techniques which would help in resisting spear phishing attacks (Xu et al., 2023). This can be achieved by using the Go-Phish filter on the machine which can automatically detect and alert users as soon as the phished URL is detected. It can be performed on the cloud platform where the apache2 server can be configured along with an anti-phishing filter to protect against phishing attacks (Yoo & Cho, 2022).

• To set up a virtual instance on the cloud platform with an apache2 server and anti-phishing software to detect possible phishing attacks.
• To research spear phishing and other types of phishing attacks that can be faced by victims (Al-Hamar et al., 2021).

RQ1: Are phishing attacks growing just like other cyber-attacks?

RQ2: How effective are anti-phishing filters in comparison to cyber awareness sessions?

The entire research can be conducted by adhering to quantitative research methodology which helps in justifying all research objectives and questions. The implementation of the anti-phishing filter can be done by creating a virtual instance on the cloud platform which can be configured with an anti-phishing filter. Along with this, some phishing attempts can also be performed to check whether the filter works or not (Siddiqui et al., 2022).

• Al-Hamar, Y. et al. (2021) “Enterprise credential spear-phishing attack detection,” Computers & Electrical Engineering , 94, p. 107363.
• Siddiqui, N. et al. (2022) “A comparative analysis of US and Indian laws against phishing attacks,” Materials Today: Proceedings , 49, pp. 3646–3649.
• Xu, T., Singh, K. and Rajivan, P. (2023) “Personalized persuasion: Quantifying susceptibility to information exploitation in spear-phishing attacks,” Applied Ergonomics , 108, p. 103908.
• Yoo, J. and Cho, Y. (2022) “ICSA: Intelligent chatbot security assistant using text-CNN and multi-phase real-time defense against SNS phishing attacks,” Expert Systems with Applications , 207, p. 117893.

Evaluating the effectiveness of distributed denial of service attacks

The given research topic is based on evaluating the effectiveness of distributed denial of service attacks on cloud and local environments. Hence, this research can be carried out using a quantitative research method. Cyber attackers find DDoS as one of the most dangerous technological exploitation when it comes to impacting network availability (Krishna Kishore et al., 2023). This research can revolve around scrutinizing the impact of DDoS attacks on the local environment and cloud environment. This can be done by executing DDoS attacks on a simulated environment using hoping or other software(s) to check where it has a higher magnitude (de Neira et al., 2023).

• To set up a server on the local and cloud environment to target using DDoS attacks for checking which had experienced slowness.
• To determine types of DDoS attack types, their magnitude and possible mitigation techniques.

RQ: Why do DDoS attacks have dynamic nature and how is it likely to sternly impact victims?

The experimentation for this research can be executed by creating a server on the local and cloud environment. Hence, this research can be carried out using a quantitative research method. These servers can be set up as web servers using apache 2 service. On the other hand, a Kali Linux machine can be configured with DDoS execution software. Each server can be targeted with DDoS attacks to check its effectiveness (Benlloch-Caballero et al., 2023).

• Benlloch-Caballero, P., Wang, Q. and Alcaraz Calero, J.M. (2023) “Distributed dual-layer autonomous closed loops for self-protection of 5G/6G IOT networks from distributed denial of service attacks,” Computer Networks , 222, p. 109526.
• de Neira, A.B., Kantarci, B. and Nogueira, M. (2023) “Distributed denial of service attack prediction: Challenges, open issues and opportunities,” Computer Networks , 222, p. 109553.
• Krishna Kishore, P., Ramamoorthy, S. and Rajavarman, V.N. (2023) “ARTP: Anomaly-based real time prevention of distributed denial of service attacks on the web using machine learning approach,” International Journal of Intelligent Networks , 4, pp. 38–45.

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Newest AI Research Topics

Research in every field is becoming more and more essential because of constant developments around the world. Similar is the case in the field of networking. This is the reason; students who are preparing to master the field of networking need to keep their knowledge of the current state of the art in the field up to date.

However, choosing the right research topic often becomes a tough task for students to carry out their research effectively. That being the case, this list contains 15 latest research topics in the field of networking. Whether you are a seasoned researcher or just starting, this list can provide you with ample inspiration and guidance to drive your research forward in the dynamic and evolving field of Networking.

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Conceptualizing and Advancing Research Networking Systems

Science in general, and biomedical research in particular, is becoming more collaborative. As a result, collaboration with the right individuals, teams, and institutions is increasingly crucial for scientific progress. We propose Research Networking Systems (RNS) as a new type of system designed to help scientists identify and choose collaborators, and suggest a corresponding research agenda. The research agenda covers four areas: foundations, presentation, architecture , and evaluation . Foundations includes project-, institution- and discipline-specific motivational factors; the role of social networks; and impression formation based on information beyond expertise and interests. Presentation addresses representing expertise in a comprehensive and up-to-date manner; the role of controlled vocabularies and folksonomies; the tension between seekers’ need for comprehensive information and potential collaborators’ desire to control how they are seen by others; and the need to support serendipitous discovery of collaborative opportunities. Architecture considers aggregation and synthesis of information from multiple sources, social system interoperability, and integration with the user’s primary work context. Lastly, evaluation focuses on assessment of collaboration decisions, measurement of user-specific costs and benefits, and how the large-scale impact of RNS could be evaluated with longitudinal and naturalistic methods. We hope that this article stimulates the human-computer interaction, computer-supported cooperative work, and related communities to pursue a broad and comprehensive agenda for developing research networking systems.

1. INTRODUCTION

Over the past several decades, science has become significantly more collaborative [ Adams et al. 2002 ; Arzberger and Finholt 2002 ; Katz and Martin 1997 ; Rhoten 2007 ; Zerhouni 2003 ]. Increases in the number of international collaborations, coauthored papers, and multi-investigator grant proposals are evidence for this trend [ Olson et al. 2008a ], as is the rising frequency of terms such as “interdisciplinarity” and “multi-disciplinarity” in the literature [ Braun and Schubert 2003 ]. Olson et al. cite multiple reasons for this development: “the urgency, complexity and scope of unsolved scientific problems; the need for access to new, and often expensive, research instruments and technologies; pressure from funding agencies; and information and communication technologies that facilitate interaction and sharing” [ Olson et al. 2008a ]. Therefore, collaboration among the right individuals, teams, and institutions is becoming ever more crucial for progress in science.

Finding “optimal” (regardless of how one defines the term) collaborators, however, is difficult, and becoming more so [ Schleyer et al. 2008a ; 2008b ; Spallek et al. 2008 ]. Establishing collaborations is a labor-intensive and risky process, especially when multiple disciplines are involved. Collaboration seekers often struggle with the target disciplines’ terminology, have difficulty identifying true experts, and lack relevant social contacts. In addition, they must assess potential collaborators in light of many criteria [ Schleyer et al. 2008b ], a process impeded by incomplete, fragmented information. Finally, reviewing potential collaborators does not scale well. Assessing the n th candidate takes as much work as assessing the first. At the same time, the universe of collaborative opportunities continues to expand as information about researchers becomes more accessible and remote collaborations become more feasible [ Katz and Martin 1997 ].

Recently, the term “Research Networking Systems” (RNS) became popular to describe electronic systems designed to help researchers find collaborators. “Research networking system” emerged as an alternative to “research collaborator discovery system,” “expertise location system,” and other terms after the National Center for Research Resources awarded a $12m grant to the University of Florida to develop a national prototype system 1 . The request for applications solicited proposals to develop “infrastructure for connecting people and resources to facilitate national discovery of individuals and of scientific resources by scientists and students to encourage interdisciplinary collaboration and scientific exchange” [ National Center for Research Resources 2009 ].

In light of this goal, we propose the following definition for RNSs.

Research Networking Systems (RNS) are systems which support individual researchers’ efforts to form and maintain optimal collaborative relationships for conducting productive research within a specific context.

Several aspects of the definition are noteworthy. While RNSs can serve other purposes, such as managing a university’s research portfolio, the primary users whose needs must be met are “individual researchers.” RNSs are intended to help “form and maintain” relationships, not complete collaborative tasks. “Collaborative relationships” refer to the interpersonal ties that support successful research collaborations. While the nature of these relationships is subject to ongoing debate, our definition assumes that they involve shared, two-way interests; ongoing, often sporadic, interaction; and the creation of joint work products. “Optimal” is a subjective and situational measure, yet searching for the best possible opportunities is central to RNSs. The aspect of “productive” research speaks to the collaboration outcomes. While papers, presentations, and other scientific artifacts are generally accepted metrics of research productivity, they are arguably imperfect. Lastly, “context” is included in the definition of RNSs because of its importance in shaping research collaborations. Context includes factors such as researchers’ needs and goals, project characteristics, organizational policies, disciplinary norms, and institutional constraints. Successful RNSs provide the information individual researchers need to develop and maintain contextually embedded collaborative relationships.

The goal of this article is to stimulate foundational research on research networking systems that takes into account what is known about collaboration, expertise location, and social networking. We hope to challenge researchers in multiple fields by proposing claims and corresponding research questions that can be tested and/or investigated.

Two considerations must be mentioned to put the proposed research agenda in context. First, in reviewing the literature, we draw from studies of many scientific disciplines, including computer science and biomedicine. Disciplinary culture, values, and norms have a significant impact on collaborative relationships. In order to frame the discussion, this article uses examples from biomedical research. In consequence, the relative importance of the issues we identify may vary in other disciplines. Second, we discuss RNSs primarily in the context of academic research. While the proposed research agenda may be applied to corporate research and development environments, academic research domains are complex and distinct enough to merit separate consideration.

2. RESEARCH NETWORKING AND COLLABORATOR DISCOVERY

Literature relevant to RNSs includes topics such as expertise location systems, formation of scientific collaborations, and the use of technology in research collaborations and social networking. In particular, research on expertise location and sharing [ Ackerman et al. 2003 ; McDonald and Ackerman 1998 ] informs the discussion of RNSs because finding collaborators involves searching for individuals with specific expertise. We therefore review prior work on expertise location systems before discussing existing RNSs.

2.1. Expertise Location vs. Research Networking

Expertise location is a concern in several contexts, including “expertise locating systems” [ McDonald and Ackerman 2000 ], “knowledge communities” [ de Vries and Kommers 2004 ; Erickson and Kellogg 2003 ], and “communities of practice” [ Johnson 2001 ; Millen et al. 2002 ]. Zhang et al [2007] defined Expertise Locator Systems (ELS) as “CSCW systems that help find others with the appropriate expertise to answer a question.” In a review of contemporary ELSs, Becerra-Fernandez [2006] described them as knowledge sharing systems that “point to experts, those that have the knowledge.” Others have defined ELSs in terms of the functions they perform. For example, ELSs can “connect people to people; link people to information about people; identify people with expertise and link them to those with questions or problems; identify potential staff for projects requiring specific expertise; assist in career development; and provide support for teams and communities of practice” 2 .

The CSCW literature contains numerous references to expertise location and the design of expertise location systems [ Ackerman and Palen 1996 ; Ehrlich et al. 2007 ; Friedman et al. 2000 ; Jacovi et al. 2003 ; Mattox et al. 1999 ; McDonald and Ackerman 2000 ; Mockus and Herbsleb 2002 ; Streeter and Lochbaum 1988 ]. This body of work can help us compare and contrast ELSs and RNSs.

First, locating an expert and establishing a research collaboration both involve looking for and discovering expertise. The focus of expertise location is finding an answer, a solution, or a person with whom details of a problem can be discussed [ Ehrlich et al. 2007 ]. The need is largely determined by the task at hand. This emphasis is reversed when forming research collaborations. Researchers looking for collaborators primarily seek a person to establish a relationship with. The specific task or problem is secondary to forming and maintaining this relationship.

Second, the comparatively shorter time horizon of interaction in expertise location allows for benefits which are more asymmetrically distributed. Individuals looking for an answer often stand to gain more than the experts providing it [ Lakhani and von Hippel 2003 ]. In research collaborations, on the other hand, benefits must be more evenly distributed because they often span multiple collaborative tasks and projects, and extended time frames.

Third, ELSs are designed for situations where the goal is defined but needed knowledge is “hidden.” To succeed, individuals must extract answers from the set of available experts. In contrast, scientific researchers often work with ill-defined questions and objectives that shift over time. These collaborative relationships reflect the nature of scientific inquiry in which large problems are pursued incrementally in a meandering, exploratory fashion. The query-driven approach is complemented by an opportunity-driven one, with new directions emerging serendipitously as methods and concepts developed in one area find novel uses in another.

Last, in industry, where most ELSs are deployed, individuals typically work within a single organization. Project assignments, team memberships, and immediate colleagues are determined by management. In academia, scientists often work across institutional boundaries [ Cummings and Kiesler 2005 ; Olson et al. 2008b ] and have significant autonomy when selecting their projects, affiliations, and collaborators.

In summary, while ELSs and RNSs have common functions, they also differ significantly with respect to user characteristics, organizational context, and the goals they serve. Thus, while prior work on ELSs provides a useful starting point for discussions of RNSs, we must also consider systems specifically designed for supporting research networking.

2.2. Current Research Networking Systems

While there is a relatively large body of literature on expertise location systems [ Ackerman et al. 2003 ; Becerra-Fernandez 2006 ], studies of research networking systems are rare. A focused literature search identified descriptions of only five systems which have been tested and/or implemented. Several other recently developed systems have not been described in the literature.

At the University of Pittsburgh, an application called Faculty Research Interests Project (FRIP) helps faculty establish collaborations [ Friedman et al. 2000 ]. FRIP indexes faculty research interests using Medical Subject Headings (MeSH) [ Coletti and Bleich 2001 ] and draws on MEDLINE-indexed publications to populate its database. In 2000, FRIP indexed 1,925 research faculty at the six schools of the University of Pittsburgh’s Health Sciences Center. FRIP’s functionality is currently being replaced by Pitt’s Digital Vita (see the following) system.

A second recently developed tool for helping connect researchers with shared interests is a Facebook application called MEDLINE Publications (MP) [ Bedrick and Sittig 2008 ]. The system uses the PubMed database to automatically create user-customizable lists of publications. The system includes a rudimentary recommendation algorithm to identify other users with similar publication profiles. Like FRIP, MP uses MeSH as the controlled vocabulary for specifying research interests. MP has attracted a reasonable user base, and anecdotal evidence suggests that it has been useful to some.

A third research networking system is Searchable Answer Generating Environment (SAGE), a searchable repository of funded research information for all universities in Florida [ Becerra-Fernandez 2006 ]. This system implements a distributed database schema that can be searched by criteria such as research topic, investigator name, funding agency, and university. To keep the data repository current, participating institutions must provide funding data on an ongoing basis. Researchers across Florida benefit from SAGE increasing their visibility and facilitating efforts to locate potential collaborators at other universities, in industry, and in federal agencies. SAGE has also been used by NASA and small businesses to identify university researchers for collaboration. As of 2006, the SAGE database included about 7,817 researchers and 53,124 projects from fourteen institutions throughout Florida.

Liu et al. [2005] described a system that uses RDF (Resource Description Framework) for expertise matching by integrating data from multiple, heterogeneous sources and making them available through concept-based searches. An initial prototype system was evaluated in the School of Computing at the University of Leeds. Results indicate that the RDF-based expertise matching system outperforms traditional DBMS techniques because it improves match accuracy and facilitates expertise selection.

Last, Schleyer and colleagues [2008a] proposed the Digital Vita system as a prototypical design and architecture responsive to initial requirements for research networking [ Schleyer et al. 2008b ]. Digital Vita includes four main functions: maintaining, formatting, and semiautomated updating of biographical information; searching for researchers; building and maintaining social networks; and managing document flow. The system departs from other approaches for representing researchers in that it is built around a researcher’s academic Curriculum Vitae (CV). While not perfect, the CV is often the most up-to-date and comprehensive document describing a scientist’s accomplishments and activities. With its focus on CV maintenance, integration with the local context, and provision of benefits for individual researchers, Digital Vita has the potential to reduce adoption barriers, represent researchers more comprehensively than keyword-based profiles, and achieve ongoing system utilization.

In addition to the five systems described in the literature, several other research networking systems exist in academia and industry. Academic systems include the University of Florida’s VIVO 3 project [ Gewin 2010 ], Harvard’s Catalyst Profiles 4 , and the University of Iowa’s Loki 5 . The Distributed Interoperable Research Experts Collaboration Tool (DIRECT) 6 is a recent initiative to allow users to search for experts across these systems. Commercial systems include the Community of Science ( http://www.cos/com ), Index Copernicus Scientists ( http://scientists.indexcopernicus.com/ ), Research Crossroads ( http://www.researchcrossroads.com/ ), BiomedExperts ( http://www.biomedexperts.com/ ), and Epernicus ( http://www.epernicus.com ).

Each of these systems has a different approach for creating searchable directories of researchers. As a result, they provide useful insights into the architectural and data management problems associated with gathering and storing researcher profiles. However, as with expertise location systems, the research networking systems described in the literature only partially address the requirements of research networking.

2.3. Research Networking Challenges in Biomedical Sciences

While the marketplace and academic institutions have begun implementing expertise-focused research networking systems, there is a need for theories and models to inform RNS design, implementation, and evaluation. No extant studies directly consider RNSs. Nonetheless, the literature on scientific collaboration and collaboration formation provides some insight into the problems that RNSs are intended to address.

A recent study by Weng et al. [2008] showed that collaboration on cross-cutting research topics such as obesity is not well served by the traditional organization of biomedical research institutions. The authors identified obesity researchers using several search strategies (Google, PubMed, and snowball sampling) and surveyed them to determine departmental/center affiliation, collaborators, and research interests. Participants were distributed over multiple departments and often affiliated with more than one research center. Respondents who collaborated with others had 8.8 collaborators on average, indicating a relatively active community. Some research groups, however, were only connected by a single pair of individuals. Institution-level success factors for interdisciplinary collaboration suggested by the study included “(1) establishment of interdisciplinary research centers; (2) identification of boundary spanners who link dispersed research communities; and (3) creation of scientific journals that publish transdisciplinary research results.” The findings of this study suggest that interdisciplinary collaborations could be organized as “virtual teams” [Hinds et al. 2002].

In a more general attempt to understand how research collaborations are formed in the health sciences, Spallek and colleagues [ Schleyer et al. 2008a ; 2008b ; Spallek et al. 2008 ] conducted semistructured interviews with 27 biomedical scientists at the University of Pittsburgh. The study focused on general aspects of subjects’ collaboration activity, such as who they were currently collaborating with, what motivated them to seek collaborators, and how they searched for them. Four main groups of factors were found to affect collaboration-seeking: motivation, evaluation, search and selection, and barriers. Participants who reported using directories such as FRIP or Community of Science noted that they were useful for people new to an institution and for finding individuals outside the immediate work context. However, researcher directories were seen as limited because of incomplete coverage of research domains; sparse, outdated researcher profiles; and lack of support for leveraging social networks. Although this study did not focus on research networking systems, its results suggest that developing and refining such systems would have significant practical utility.

In parallel, our research group also formulated an initial set of requirements for collaborator discovery systems in biomedical science [ Schleyer et al. 2008b ]. The study used affinity diagramming, literature reviews, contextual inquiries, and semistructured interviews to develop a list of requirements for systems for finding collaborators. The requirements include: a good cost/benefit ratio for the user when creating and updating online profiles; representation of researchers through rich, comprehensive, and up-to-date information; exploitation of social networks; assessment of potential collaborators’ “soft” traits, such as personality and work styles; use of multiple indicators of past collaboration activity; user-modifiable preferences regarding privacy and public availability of profile information; effective cross-disciplinary search; and active highlighting of “nonintuitive” connections between researchers.

Existing studies show that RNSs must function in a complex socio-technical context. They are subject to multiple, sometimes conflicting, requirements that must be balanced carefully in order to maximize system utility for all user populations. While there is a growing body of work which examines the factors underlying effective research collaborations, many unanswered questions remain about how to best use information technology to facilitate research networking.

3. RESEARCH AGENDA FOR RESEARCH NETWORKING SYSTEMS

The following research agenda is organized around four areas that contribute to RNS success: foundations, presentation, architecture , and evaluation . Foundations addresses theoretical models, core principles and general factors that underlie the design of effective RNSs. Presentation examines issues concerning user interfaces, interaction design, and system functionality. Architecture discusses the internal design of RNSs, how they interact with external information sources, and interoperability. Finally, evaluation is concerned with how RNS outcomes can be framed and measured.

While the proposed categorization of the particular claims and research questions may be debated, the four areas are critical aspects of RNS design and implementation. They support both targeted investigation of issues and identification of useful links to the diverse body of existing research. In each area, we posit claims regarding the nature of collaborative relationships and RNSs. Each claim is followed by a brief review of the relevant literature and a list of open questions which, if addressed, would significantly improve our ability to design, implement, and evaluate research networking systems. The goal of this research agenda is to advance the study and development of RNSs, and to make them a useful part of the scientific enterprise. Hence, the open questions were selected to focus attention on issues particular to RNSs as opposed to related systems, such as virtual communities, expertise location, and cooperative work.

3.1. Foundations

While it may be convenient from a systems design perspective to conceptualize research networking as a search or information display problem, RNSs must support a more complex set of social behaviors. In this section we describe three foundational perspectives on collaborative relationships, and examine their implications for the design and evaluation of RNSs.

To form collaborative relationships, individuals must balance the different motivations of potential collaborators in the context of projects, institutions, and disciplines.

Many researchers have proposed models for describing effective collaborations [ Suchman and Trigg 1986 ]. Existing frameworks focus on various aspects of collaboration, including key concepts/variables at work in research collaborations [ Katz and Martin 1997 ; Larson 2003 ; Melin 2000 ; Suchman and Trigg 1986 ], participants in a collaboration and the division of labor [ Jenerette et al. 2008 ; Kouzes et al. 1996 ], and the process of collaboration and activities involved at each stage [ Gitlin et al. 1994 ; Kraut et al. 1987 ]. In part, this body of work has also explored the motivations and mechanisms underlying collaboration formation.

At societal level, researchers have examined the transformation of modern science and the social, cultural, and technological factors that drive collaboration [ Börner et al. 2010 ]. These factors include use of expensive, sophisticated instrumentation [ Olson et al. 2008a ]; more emphasis on application; greater specialization and concentration of resources [ Ziman 1994 ]; changing patterns and levels of funding; and the growing professionalism of science [ Katz and Martin 1997 ]. However, the move towards a greater degree of collaboration in science is not without problems [ Cummings and Kiesler 2007 ]. Multi-university collaborations face significant coordination challenges which, if not addressed, can lead to suboptimal project outcomes [ Finholt and Olson 1997 ].

At project level, factors that affect collaboration include problem complexity and scale, division of labor, and degree of specialization [ Laudel 2002 ; Rhoten 2007 ]. Institutional factors that influence collaboration activity include role specialization [ Madanmohan and Navelkar 2004 ], the nature of the work [ Birnholtz 2007 ], the radicalness of the research [ Belkhodja and Landry 2007 ], access to particular resources [ Mattessich and Monsey 1992 ], structural characteristics of organizations [ Walsh and Maloney 2007 ], organizational processes [ LeGris et al. 2000 ], organizational management and support [ Millen et al. 2002 ], and funding contingencies [ Bos et al. 2007 ]. Many things which motivate individual scientists to collaborate, such as the need for knowledge, expertise, and skills [ Beaver 2001 ]; access to special equipment and funding [ Melin 2000 ]; the desire for social relationships [ Fox and Faver 1984 ; Terveen and McDonald 2005 ]; and the need to educate and mentor students [ Katz and Martin 1997 ; Melin 2000 ], are directly linked with these project and institutional factors.

As with any long-term relationship, collaborations can only be maintained if the work and incentive structures are aligned so that all of the involved individuals benefit from participation [ Numprasertchai and Igel 2005 ]. Successful RNSs must support individuals’ efforts to identify potential collaborators whose needs and incentives complement their own. Through rich user models and appropriately designed profiles, RNSs can leverage information about institutional, project, and individual factors to help collaboration seekers’ detect when and where collaboration is useful and feasible. This suggests that the following questions are central to the design and creation of effective RNSs.

• —How should RNSs model user characteristics known (or hypothesized) to affect willingness of individuals to engage in collaborations? While a variable such as “age” is easy to model, others like “seniority” or “technical competence” are more difficult to represent.
• —How should RNSs incorporate project-related, institutional, social, structural, and cultural characteristics which affect individuals’ motivation to participate in collaborative relationships?
• —How should RNSs model the conditions under which researchers start looking for a collaborator? Is a single model sufficient? How should the model evolve over time as careers, accomplishments, and interests change?

Exploiting social networks is essential for efficient and effective research networking.

People work within social networks. Although these networks may cross organizational boundaries and span geographic distance, individuals are still constrained by who they know and what they know about them. As a result, many expertise location systems developed in recent years have integrated social network information to help evaluate potential experts and facilitate communication with them [ Kautz et al. 1997a ; Ogata et al. 2001 ]. McDonald [2003] compared two different social networks as alternative bases for recommending experts within a medical software company. The first network, based on shared work contexts, captured network ties arising from work arrangements. The second, the socializing network, linked individuals who interacted socially. The results illuminated a number of critical issues to consider in development of RNSs. Using network information forced a trade-off between finding the most knowledgeable person and finding the person with whom the searcher could most easily interact. Also, users still sometimes desired broader recommendations even if the system’s recommendations were appropriate. Lastly, users often preferred their own egocentric social network over the one generated and recommended by the system.

In another system, Yang and Chen [2008] developed a mathematical model of a three-layer social network to support interactive collaboration, taking into account the knowledge relationship and social relationship ties of potential collaborators. In this system, a peer-to-peer knowledge net is overlaid with the peer-to-peer social net. An Instant Messaging (IM) system helps individuals communicate with peers identified through the social network. Preliminary evaluation of this system with student users showed that most were willing to use this system to find others open to sharing their knowledge.

Many methods for gathering social network information have been suggested. Social networks have been constructed based on email exchanges among individuals [ Ogata et al. 2001 ], Web pages related to a person, the Database systems and Logic Programming (DBLP) bibliographic information service for computer science, and the publication ranking list from Citeseer [ Li et al. 2007 ]. Pavlov and Ichise [2007] built link predictors which identify potential collaboration opportunities using the structural information in coauthorship networks. However, social networks derived from coauthorship are likely to be imperfect representations of a researcher’s collaborative relationships [ Katz and Martin 1997 ]. To overcome this problem, McDonald and Ackerman [2000] used participant observation, formal and informal interviews, and pile sorts. Yang and Chen [2008] had users fill out forms and answer questions about peers’ knowledge and social ties. The Digital Vita system blends the two strategies, allowing researchers to specify collaborative relationships explicitly through “colleague requests” (equivalent to “friend requests” in Facebook) [ Schleyer et al. 2008a ] while also deriving implicit ties such as coauthorship and shared department membership from CVs.

In traditional social networks, individuals rely on their contacts to provide access to a wide range of information and opportunities [ Adler and Kwon 2002 ]. Supporting searches within a network is an important part of facilitating collaboration formation. Previous research on search strategies in social networks has identified two main approaches. The first is automation of the small-world approach, where the target is known by name or a unique identifier [ Adamic and Adar 2005 ; Yang and Garcia-Molina 2002 ]. Adamic and Adar [2005] simulated small-world experiments on an email network in an organization and a student social networking system Web site. They found that small-world search strategies using a contact’s position in physical space or an organizational hierarchy could effectively locate the most appropriate individuals. However, in a social network where hierarchical structures were not well defined, local search strategies were less effective.

A second approach for searching within a social network focuses on locating a person with specific expertise or knowledge. Zhang and Ackerman [2005] evaluated three families of strategies for searching using social network information. These strategies were based on computation, network structure, or individual similarity. The computational approach, for instance, used breadth-first Search to broadcast a query to a person’s neighbors. Information scent search, on the other hand, selected the person with the highest match score between the query and his profile [ Yu and Singh 2003 ]. In a simulation on an organization’s email dataset, the different strategies affected the search process in important ways. For example, weak ties [ Granovetter 1973 ] appeared more effective for seeking new information, but the relative rank of different algorithms changed little when examining social costs.

The importance of existing network structures in formation of collaborations suggests that the following questions are critical for design of effective RNSs.

• —How can information about researchers’ social and collaborative networks be gathered and maintained efficiently? How can implicit relationships, such as coauthorship, be refined and/or augmented to serve as a basis for constructing social networks?
• —How can explicit relationship identification be applied in RNSs? Should network size be limited to avoid “colleague inflation”?
• —How should social network data be used to support collaboration seeking? Should users be encouraged to focus on relatively small social distances [ Schleyer et al. 2008a ] or explore lengthy referral chains [ Kautz et al. 1997b ]?
• —How should existing and potential collaborative relationships be represented? Should weak ties be distinguished from (and perhaps given priority over) strong ties? Should potential collaborators be ranked based on the number of current collaborators (i.e., network degree)?
• —How can boundary-spanning individuals be identified and leveraged in order to generate collaboration opportunities?

Establishing collaborations requires individuals to form impressions of and evaluate potential collaborators based on information beyond expertise and interests.

In the research collaboration literature, few studies have focused on the initiation of collaborations. Kraut et al. [1987] suggest that collaboration formation is more a process than an event. The initiation stage involves both relationship- and task-related activities. For the relationship, the essential activity is determining whether potential collaborators are acceptable partners. At task level, participants must identify collective research objectives and formulate specific work plans. If a collaboration is to succeed, researchers must develop from mere acquaintances to committed partners. Kraut identified two paths for this process. For some researchers, the initial contact evolves into joint commitment the way a bilateral friendship develops. For others one partner proposes collaboration just like during an asymmetric courtship ritual. Whichever way collaborations develop, serendipitous and informal conversations are an important early step.

Prior studies have examined a variety of factors that affect how prospective collaborators are evaluated, including competence, complementarity, work and collaboration styles [ Axelrod 1984 ], personality, and physical proximity [ Kraut et al. 1987 ]. Schleyer et al. [2008b] identified support for compatibility assessment as a key element of RNS requirements. For instance, the system should enable users to find collaborators compatible in personality, work style, and other factors. An individual’s likely availability, accessibility, and willingness to engage in collaboration also might impact her selection as a potential collaborator. Several studies found that researchers trust personal recommendations when assessing compatibility with potential collaborators [ Beaver 2001 ; Flynn 2005 ]. Interaction with potential collaborators is another way researchers gather information about their compatibility. Face-to-face interaction seems to produce the highest trust among unfamiliar collaborators [ Moore et al. 1999 ]. In the absence of face-to-face opportunities, strategies such as chat sessions and exchange of personal information can also help to overcome limited availability of information [ Zheng et al. 2002 ]. While there are a range of evaluation criteria, their relative importance appears to be context- and situation-dependent. For instance, work style compatibility may only be a minor constraint for a collaboration based on sharing equipment or other scarce physical resources [ Spallek et al. 2008 ].

While much research in the expertise location system literature has focused on expertise representation, there is evidence from studies of work relationship formation that expertise may sometimes be a secondary concern when selecting collaborators [ Casciaro and Lobo 2005 ; 2008 ]. Studies of social matching, such as the work of Terveen and McDonald [2005] , show that personal characteristics must be taken into account during the matching process. This suggests that providing information, either directly or indirectly, about traits such as personality, friendliness, character, trustworthiness, sense of humor, and work style may be relevant in the design of RNSs. The apparent difficulty of obtaining information about these traits is one reason why social connections are so important in collaborator discovery: they can be a source of information about personal traits. The importance of this information in collaborative relationship formation suggests that the following research questions are central to the study of RNSs.

• —What collaborator traits, other than expertise and interests, are useful in making collaboration decisions?
• —How can traits such as productivity, work style, adherence to deadlines, organization, communication style, conflict resolution skills, and personality be assessed, modeled, and presented? Which traits should be highlighted in interfaces designed to support evaluation of potential collaborators?
• —What features and technologies are best suited for supporting joint exploration of relationship and task issues during the initial stages of collaboration formation?

While they are not the only possible characterizations of collaborative relationships, the three theoretical perspectives presented here (collaborative relationships as balanced incentive structures; embedded social ties; and the result of impression formation) provide a foundation for defining requirements for RNSs.

3.2. Presentation

At their core, RNSs are systems that capture, store, and present data about people and relationships. The interface used interact with these data significantly affects how an RNS influences users’ efforts to form and maintain collaborations. In this section, we consider aspects of presentation and representation that theory and prior work suggest will be critical for the creation of successful RNSs.

RNS must describe potential collaborators’ expertise and interests in a comprehensive and up-to-date manner.

Early attempts at compiling representations of expertise relied on data provided by the user, typically in the form of profiles. HelpNet, for instance, asked users to fill in and maintain profiles [ Maron et al. 1986 ]. This approach, however, often suffers a lack of compliance [ Ehrlich 2003 ]. As a result, much attention has been devoted to the automated acquisition of expertise information. Sources used include published documents such as resumes [ Becerra-Fernandez 2006 ]; Wikipedia content, discussions, and user data [ Demartini 2007 ]; literature databases [ Friedman et al. 2000 ]; newsgroup postings [ Terveen et al. 1997 ], and online community site data [ Bojars et al. 2008 ].

A key limitation of these approaches is that they conflate an individual’s credentials, expertise, and interests, each playing a different role in the evaluation of a potential collaborator. Credentials project an image of general competence in a domain, such as medicine or law. Expertise specifies knowledge and prior experience in one or more topics in that domain. Statements of interest provide information about current motivations. Collaboration seekers typically examine all three areas when assessing potential matches. For instance, a researcher’s publications provide a historical record of performance which is only useful in the context of current interests. If current interests do not match those of the collaboration seeker, even a highly productive publication record is irrelevant.

Derivation, representation, and presentation of potential collaborators’ expertise and interests are critical to the design of effective, sustainable RNSs. In light of this, we propose the following research questions.

• —How should type and extent of expertise and interests be represented to help researchers make nuanced and valid collaboration decisions?
• —Can researcher interests be inferred computationally or do they have to be specified by the user? Should both methods be used together?
• —How can the representation of a researcher’s expertise and research interests be kept up-to-date with minimal user effort? To what degree can current activity be inferred computationally, for instance, through the semantic Web [ Schleyer et al. 2008b ]? How should current and past activities be summarized and displayed to support identification and evaluation of collaboration potential?

RNS must represent individuals’ expertise, interests, and activities using controlled terminologies.

Some fields, such as biomedicine, have a strong tradition of using controlled terminologies [ Coletti and Bleich 2001 ]. Others, such as computer science, do not. Folksonomies [ Woolwine et al. 2011 ] have multiple advantages and benefits for indexing documents and people, including their authentic use of language and multiple potential interpretations. However, they also create problems for representing concepts in ways that are commonly understood [ Peters and Stock 2007 ]. Two approaches have been proposed to address the limitations of user-created tags. One is to improve users’ “tag literacy” [ Guy and Tonkin 2006 ], while the other considers tags as natural language elements amenable to automatic NLP methods [ Stock 2007 ].

A recent study by Lee and Schleyer [2012] found minimal overlap between social tags and controlled index terms for a sample of 231,388 biomedical research papers. A resulting challenge for RNSs is how to balance the effects of controlled and user-generated terminologies. Controlled terminologies are valuable because they provide high-quality information about a potential collaborator. They enable cross-disciplinary searches, support identification of synonyms and related terms, and facilitate automatic discovery of otherwise undetected similarities between individuals. Yet, controlled vocabularies necessarily place constraints on individuals’ ability to describe their expertise, interests, experiences, and characteristics in their own terms. To the degree that research networking is a process of impression management and impression formation, use of controlled vocabularies may be perceived by RNS users and subjects as unnecessarily limiting. The potentially conflicting implications of controlled vocabularies in the context of RNS suggest the following research questions.

• —Are existing controlled terminologies and taxonomies for indexing publications, such as the Medical Subject Headings [ Coletti and Bleich 2001 ] and the ACM Computing Classification System, adequate for representing individuals’ expertise, interests, and characteristics? If not, how should they be improved or expanded?
• —How should expertise and interests be represented in domains which lack widely accepted controlled terminologies?
• —What are the strengths of folksonomies and social tagging for representation of individual researchers? When and how should controlled and user-generated terms be combined in researcher profiles?
• —How does use of controlled terminologies affect individuals’ willingness to use, create and maintain profiles within an RNS?

RNSs must allow users to search and visualize researcher profiles in multiple ways.

RNSs are designed, in part, to make the large search spaces of potential collaborators tractable and accessible. A tension exists between focused result sets, in which the system provides a few, presumably high-quality, matches and broader ones, which require more user effort to explore. McDonald’s work [ McDonald 2003 ] suggests that RNSs should allow user experimentation and adaptation of the system for different purposes.

Previous work suggests that allowing users to apply different types of criteria may be beneficial. The Expertise Recommender [ McDonald and Ackerman 2000 ] offers “Departmental” and “Social Network” as filters for system recommendations. The Small-Blue system implements a social-context-aware expertise search system that presents an unfiltered list of experts with information about the degree of separation, allowing the user to select the “right” person using social connection information [ Ehrlich et al. 2007 ].

RNSs must incorporate and combine traditional methods of locating collaborators, such as social networks and expertise database searches. RNSs which treat collaborator identification as a decontextualized search process based on impersonal expertise profiles are unlikely to have much impact on users’ relationship formation and maintenance activities. Yet, RNSs which only reveal opportunities in the user’s immediate social context will overlook potentially fruitful chances for novel and interesting relationships. Taken together, these issues suggest the following research questions regarding the need for diverse presentation and discovery strategies in RNSs.

• —What different strategies should RNSs support for locating collaborators? When are strategies based on information artifacts, general profiles, and/or existing network structures most effective?
• —What types of filters and representation are most useful to users when navigating the research collaboration search space?
• —Should the presentation of RNS information depend on user characteristics, project features, or disciplinary norms? What are the primary dimensions that can be varied to create high-impact, individualized representations?
• —Which search algorithms minimize the user effort required to search efficiently and effectively for collaboration opportunities?

RNSs must balance the tension between seekers’ need for comprehensive information and potential collaborators’ desire to control how they are seen by others.

A collaboration seeker’s desire for comprehensive information needs to be balanced with potential collaborators’ requirements for privacy and access control [ DiMicco and Millen 2007 ; Hewitt and Forte 2006 ]. Privacy is not as central in expertise location systems as it is in RNSs [ Bellotti 1996 ; Fogel and Nehmad 2009 ] because expertise location focuses on task-oriented, episodic interactions. The long-term relationships that RNSs help establish, on the other hand, are central to an individual’s professional identity, career success, and self-efficacy. As a result, how an individual is presented to and seen by others in an RNS is an important factor [ Goffman 1959 ; Leary 1996 ; Schlenker 2003 ; Schlenker and Leary 1982 ].

Being visible and accessible in an RNS also carries different costs depending on individual characteristics. To some, the benefits of greater visibility outweigh any potential costs [ Gross et al. 2005 ]. Others may find the loss of privacy and control unacceptable [ Mann 2007 ; Rosenblum 2007 ]. A senior scientist with many existing collaborations may want to be less visible than a junior scientist for whom exposure can be advantageous. Thus, availability of privacy and access controls may be critical for an individual’s willingness to participate in an RNS.

Taken together, these issues suggest a fundamental tension in RNS design. For individuals seeking to form collaborations, the value of an RNS increases if it can provide comprehensive information about potential collaborators. However, the individuals being profiled may be wary of a public presentation of their expertise, interests, past activities, and personal characteristics that encourage detailed comparisons with others. Effective RNSs must balance the needs of both collaboration seekers and potential collaborators. This requirement suggests the following questions.

• —How does a researcher’s willingness to share different types of profile information vary and what are implications for RNS design? For instance, while researchers are unlikely to object to sharing public information, under what conditions will they be willing to share information about current research projects?
• —How do individuals react when information from many public sources about them is presented in one place?
• —How does the willingness to share information vary with the personal, social, and organizational distance to others? For instance, are researchers more or less willing to share information with other researchers in their home discipline?
• —How should RNS allow researchers to control privacy and public availability of information about themselves? How much control is reasonable without reducing the system’s utility?

RNSs should support serendipitous discovery of collaborative opportunities.

While query-driven interfaces play an important role in supporting research networking, effective RNSs must also promote appropriate serendipitous discovery. Like successful entrepreneurs [ Gaglio and Winter 2009 ], high-impact researchers are able to accomplish their goals in part because they can recognize and capitalize on emerging opportunities not obvious to them. Although deliberate planning and intentional search are an important part of forming collaborations, so too is the ability to identify and respond to unanticipated opportunities that emerge from the complex social, institutional, and intellectual environments in which research takes place. To fully support researchers’ efforts to form collaborative relationships, RNSs must facilitate both the intentional and serendipitous discovery of potential collaborators.

Matching services have been used with success in many social contexts, but it is less clear how they would be applied to research collaborations. The literature on social matching and collaborative support contains a number of algorithms to match potential partners [ Budzik et al. 2002 ; Pavlov and Ichise 2007 ; Terry et al. 2002 ; Zhang et al. 2007 ]. For example, Yenta is a distributed agent-based system that groups people with common interests by examining the content of their file systems [ Foner 1996 ]. MEDLINE Publications, a scientific collaboration tool built on Facebook, offers a recommendation engine that helps connect a user with others who have similar publication profiles, thereby exposing him to new potential collaborators [ Bedrick and Sittig 2008 ]. Active matching services, similar to the current awareness systems offered by many literature databases, might be set up to proactively notify users about potential collaboration opportunities. This RNS feature, if properly calibrated, would promote opportunistic formation of collaborative relationships.

Addressing the following questions could be useful in determining how RNSs can best facilitate serendipitous collaborations.

• —What algorithms are most useful for identifying potential collaboration partners? What variables should they take into account?
• —Should users be able to customize the recommendation and matching algorithms used in RNSs? What features/aspects of the matching process should be user-modifiable?
• —How can RNSs obtain and incorporate feedback about the usefulness of suggested matches [ Melin 2000 ]?
• —Can RNSs help identify the “gaps” in science which present significant research opportunities? How can results of conceptual gap analyses be combined with social network data, researcher profiles, and user characteristics to recommend meaningful novel collaboration opportunities?

The emergence of RNSs creates an opportunity for HCI researchers and developers to apply their understanding of presentation and user experience design to a problem domain that has previously only marginally been supported by technology. Novel aspects of research networking, such as presenting multidimensional researcher profiles, supporting boundary-crossing discovery, and balancing the often conflicting needs of searchers and subjects, present important design challenges. Addressing these challenges will advance our understanding of how to develop complex but usable interfaces can facilitate research networking.

3.3. Architecture

Although individual researchers have significant autonomy in determining the direction and nature of their collaborative efforts, research collaborations and the relationships that support them are solidly embedded in a web of social and institutional systems. Resources and individuals are associated with departments, labs, centers, and universities. Journals, conferences, and associations provide networking opportunities and outlets for work within specific disciplines. Corporate sponsors, government agencies, and private foundations provide resources and collect data about research activities. These overlapping institutions each have their own practices, procedures, formats, and systems for managing data, all of which place demands on researchers and affect efforts to form collaborations. To be effective, RNSs must account not only for the needs of the individual users, but also for the nature of the larger social and institutional contexts in which researchers work and live.

RNSs must integrate information from multiple systems, make use of meta-information such as indexing terms to synthesize the information, and present results in a cohesive manner.

Researchers produce many artifacts, including papers, abstracts, presentations, grant applications, Web pages, Internet postings, tools, methods, and datasets. These artifacts are stored in a variety of personal, local, regional, national, or global systems. Representing a researcher’s work comprehensively requires information from many different sources. For instance, information about a paper may reside on the author’s computer, an electronic journal Web site, and in MEDLINE, CiteSeer, and the Web of Science. Integrating data from heterogeneous sources is a significant challenge because few systems are designed to support machine-based information access or exchange.

RNSs must merge data about a person from several sources in the absence of a common identifier. One common, if mundane, example is retrieving an author’s publications unambiguously from MEDLINE [ Bedrick and Sittig 2008 ; McKibbon et al. 2002 ]. Queries for authors with common names result in many false positives which require additional processing or manual review. Similar problems on the Web have led to the emergence of semantic Web standards for data interchange and interoperation such as SIOC (Semantically Interlinked Online Communities) and FOAF (Friend-of-a-Friend) [ Bojars et al. 2008 ].

Once documents about a person have been retrieved, their content must be mean-ingfully integrated. Many domains lack the strong tradition of indexing information using controlled vocabularies that the National Library of Medicine has established in biomedicine [ Coletti and Bleich 2001 ]. Therefore, documents may be indexed using different controlled terminologies/ontologies or not at all. Various approaches have been proposed to solve this problem. Liu et al. [2005] proposed the Resource Description Framework (RDF) that combines semantically rich information with a domain ontology to facilitate integration. Cameron et al. [2007] showed how semantic annotation and FOAF can be used to determine the expertise of researchers across various areas of computer science. Jung et al.’s research [2007] discussed a method for finding topic-centric experts from open-access metadata and full text documents using OntoFrame, a semantic Web-based academic research information service. Other approaches to integrating information from multiple sources include ontology-based integration methods [ Wache et al. 2001 ], Digital Object Identifiers ( http://www.doi.org ), and persistent URL mechanisms ( http://purl.org ), MOMIS (Mediator envirOnment for Multiple Information Sources), a model of information integration based on the conceptual schema or metadata of the information sources [ Bergamaschi et al. 1999 ], and automated approaches to unifying heterogeneous information based on machine-processable meta-data specifications [ Singh 1998 ]. While these methods may be useful in particular contexts, the integration of large-scale classification systems and ontologies and, therefore, the information indexed by them, remains a fundamentally difficult problem [ Prevöt et al. 2005 ].

Being able to aggregate a scientist’s information artifacts does not mean that they can be easily synthesized into a comprehensive and coherent whole. The process is hindered because documents differ with respect to currency, validity, representation scheme, level of abstraction, audience, and focus. For instance, a list of recently published abstracts may be relatively current in representing a researcher’s interests. Nonetheless, it might not be valid if the researcher has abandoned some of the projects. Similarly, recent grants, abstracts, and papers drawn from a departmental Web site will only be useful as a source of current research interests if they can be correlated with the keyword terms that the individual has provided to describe his interests in other systems.

In addition to the technical problems of integration, RNS developers must also consider and address the social and organizational consequences of integration. Researchers are very conscious of the role that their work plays in the formation of their professional identity and reputation (see Claim 7 ). As a result, a composite profile drawing on data from multiple sources that is not under the control of the individual being profiled may create concern. This is further complicated if the technology incorporates information from systems that focus on informal or personal networking, such as Facebook and YouTube [ Bateman et al. 2011 ]. Balancing different perspectives of various information sources is critical if an RNSs are to be effective catalysts for collaborative partnerships.

This discussion suggests the following research questions about integration challenges faced by RNSs.

• —RNS that generate comprehensive profiles must acquire and integrate information from heterogeneous sources, such as CVs, MEDLINE, the NIH’s Reporter database, conference proceeding sites, online communities, and Web pages. How should RNSs interface with these sources and aggregate data about researchers?
• —How should different information artifacts about a researcher be synthesized? What attributes, such as currency, validity, representation scheme, level of abstraction, audience, and focus should be taken into account when creating comprehensive profiles?
• —Should data about researchers be managed in a central repository or using a federated approach, in which data are retrieved and synthesized on-the-fly? What issues and problems arise in managing data using either approach?
• —How should information content annotated with different types of meta-information, such as controlled vocabularies and social tags, be synthesized? How should information artifacts without meta-information be handled?
• —How does combining information from different spheres (e.g., personal and professional) affect the impressions that people form of one another?

RNS must integrate seamlessly with an individual’s workflow and the software applications that are part of it.

The scientific workflow in biomedical research and the software applications associated with it are a complex and challenging environment with which RNSs must be integrated. Researchers use a variety of tools, such as data management applications; general office applications, such as Microsoft Word and PowerPoint; reference databases, such as EndNote and CiteULike; conference and journal submission sites; and computer-supported cooperative work applications. Introducing RNSs that duplicate data entry, management, and reporting functions places unnecessarily burdens users and is likely to be met with resistance. Therefore, close integration with researchers’ existing workflows and practices is a key factor in facilitating the adoption of RNSs [ Schleyer et al. 2008a ].

In addition, RNSs must operate across organizational and disciplinary boundaries to be effective. Given the increasingly inter- and multidisciplinary nature of research, a researcher with several research interests is likely to join different communities that are independent, isolated, and supported by incompatible systems. The ability to easily bridge these systems is an essential part of facilitating cross-boundary collaborations. One attempt to solve this problem was introduced by Mitchell-Wong et al. [2007] in the OpenSocial framework. The DIRECT 7 project has also begun to interlink several major current research networking systems.

Research networking is simultaneously critical and secondary. Failure to collaborate undermines a researcher’s ability to complete many of the activities critical to successful scientific work. Hence, research networking activities are pervasive and important. At the same time, researchers do not develop collaborations for their own sake. In this sense, research networking is a secondary support activity. Successful RNS must balance these two concerns by supporting lightweight, low-impact integration between the networking system and the systems that are the primary tools of research. This suggests the following research questions regarding integration of RNS, other networking systems, and research workflow systems.

• —How should RNSs interface with each other and related systems, such as general social networking platforms? What standards for information exchange should be developed?
• —Researchers’ activities continuously produce artifacts and information that may be useful in RNS profiles. How can workflows for activities such as conducting experiments or writing a paper be leveraged to facilitate RNS profile maintenance?
• —How should RNSs integrate with other systems that researchers use in their work, both from a back-end and user interface perspective? For instance, RNSs could automatically populate an individual citation library in CiteULike or feed an expertise database for paper reviews.
• —How can RNSs help address the problem of duplicate information management requirements? For instance, academic and funding institutions require a variety of documents, performance reviews, and progress reports. How should RNS data be structured to facilitate sharing and reuse in other systems?

Research networking is an activity inherently tied to the institutional and social context. Researchers’ efforts to form and maintain collaborations are directly affected by the practices and systems around them. Successful RNSs must work with these existing systems, interconnected where the integration provides value and deliberately separate where they are able to improve on the existing capabilities. Hence, designing RNS architectures to allow for various forms of integration is essential to their ability to facilitate the formation of collaborations.

3.4. Evaluation

RNSs require buy-in from a range of stakeholders. Researchers must use the system, both maintaining their profile and searching for others. Administrators must provide the resources needed to implement RNSs, and support their integration with the systems and procedures of the local institutions. Each of these groups has different needs which may only be partially addressed by RNSs. Making the case for an RNS requires answering a range of fundamental questions about how it provides value for individuals, relationships, and organizations.

Evaluating RNS search results requires metrics which combine traditional information retrieval measures with those specific to collaboration.

Supporting collaboration seeking with an RNS requires that designers define criteria used to select candidates from the pool of available individuals. Although researchers often feel that selecting collaborators is idiosyncratic, context-specific, or even random, the capability to systematically evaluate individual profiles is critical in RNSs.

Evaluating RNSs for collaborator discovery in some ways parallels evaluating Information-Seeking Support Systems (ISSS) for Information Retrieval (IR). Models of information-seeking which can inform RNS design and evaluation include the five-stage information seeking process model [ Cole 1997 ], the Information Seeking Process [ Kuhlthau 1991 ], and the model of general information behavior [ Wilson 1999 ]. To evaluate ISSSs, Kelly et al. [2009] advocate the development of alternative user and task models, methods for assessing support of complex, evolving tasks, and longitudinal designs. As systems providing essential information to researchers to help them make decisions on potential collaborators, RNSs can be considered a type of ISSS. This suggests a need for RNS research which extends IR models to integrate models of the collaboration seeking processes, adds new evaluation methods and measures, and develops longitudinal designs with process-specific measures of learning, cognition, and engagement.

While traditional IR approaches provide a starting point for the social, relational, and instrumental aspects of collaborator discovery, critical differences between person discovery and document retrieval suggest that effective evaluation of RNSs will require fundamentally different approaches. One approach is to consider various frameworks for describing collaboration. For example, Larson [2003] identified three key components of collaboration: structure, process, and outcomes. Structure includes characteristics such as standardized methods of communicating, decision-making, and formal agreements for sharing data and other collaborative activities. Process is characterized by clear and explicit shared research goals and objectives, experience with the change process, strong and clear leadership, and efficient work procedures. Outcomes include measurable work products such as publications, dissertations, and presentations. Another framework more directly related to RNSs is the work of Kraut et al. [1987] .

Another critical aspect of RNS functionality is candidate ranking. In general, expertise location systems do not distinguish levels of expertise. Zhang et al. [2007] nonetheless have proposed an expertise-finding mechanism that can automatically infer expertise level from characteristics of postings in an online community. As a result, potential collaborators might be personalized to a candidate’s expertise level as well as to keyword similarity.

An overarching issue regarding searching in RNSs is what metrics should be used to assess the quality of the search. Measures typically used in information retrieval include recall and precision, but they require a gold standard against which they can be calculated. While it may be possible to identify a gold standard for RNS searches under narrowly scoped circumstances, such scenarios are not likely to fully reflect the range of concerns involved in forming collaborations.

RNSs depend on criteria for systematically evaluating and ranking potential collaborators to a user. As a result, the following research questions regarding candidate evaluation are central to the development of effective RNSs.

• —What model(s) of collaboration and information seeking are most appropriate and relevant to the evaluation of RNS results?
• —How should similarity and complementarity be incorporated into the metrics used by RNSs to evaluate potential collaborators? When should the similarity of two people be highly weighted? When should complementarity be emphasized?
• —What metrics are appropriate for assessing the outcome of a search for a collaborator using an RNS? Under what circumstances can IR metrics such as recall and precision be used?
• —How can process model(s) of collaboration formation inform the design of RNS evaluation metrics? For example, if we use Kraut et al.’s [1987] framework, potential questions include: What specific tasks are involved in forming a collaborative relationship? What strategies and tools do researchers use to complete each task? How does an RNS support the completion of these tasks?

Evaluation of RNSs must assess actual and perceived effects on individual users’ collaboration practices and outcomes.

In addition to evaluating the quality of potential collaborators identified by RNSs, it is necessary to assess the general effects of RNSs use on individual users. Such effects could include how individuals’ perceptions of RNS functionality and performance develop, and how these perceptions affect users’ decisions to participate as collaboration seekers, potential collaborators, or both.

Unlike traditional CSCW applications which focus on performance of tasks by members of well-defined teams, RNSs focus on facilitating a general class of social practices within a diverse, poorly defined community [ Neale et al. 2004 ]. While the general goal of RNSs is relatively clear, the particulars of how the goal is achieved, who is involved, when it is successfully achieved, and what constitutes successful use of the system are difficult to articulate. As a result, assessing RNS performance is highly complex, having more in common with evaluating medical decision support systems [ Friedman et al. 2006 ] than with evaluating traditional process-oriented applications. As with decision support systems, the evaluation of RNS faces challenges arising from crossing multiple research disciplines. As a result, to be useful for design improvement, assessment of RNSs must take into account a plethora of factors. Functional usability and perceived ease-of-use are likely important, but so too are questions of whether the system significantly impacts a researcher at various stages of a collaboration process, as well as long-term career advancement, research directions, and scientific impact.

While the primary goal of RNSs is to facilitate the formation of productive collaboration relationships, the outcome of these relationships is dependent on many other factors, including standardized communication modes, a highly efficient work process, and strong and clear leadership [ Larson 2003 ]. Given the difficulty of delineating the functional boundary between forming collaborations, maintaining the resulting relationships, and executing collaborative work tasks, it is impossible to evaluate the impact of RNSs in isolation. Therefore, it is important to define and assess variables at the various stages of collaboration that RNS may significantly impact.

Another consequence of the complexity of the collaboration formation process is that individual users will rarely have extensive, objective measures of systems performance on which to base their adoption and participation decisions. The presence of potentially conflicting user roles, that is, collaboration seeker and potential collaborator, means that past experience with the system may not be a clear indicator of future effort or outcomes. The extended timeframe of collaborative relationships and the presence of confounding factors also significantly limit an individual’s ability to accurately assess the correlation between use of a particular RNS and successful formation of a collaborative relationship. Consequently, user perceptions of system characteristics and impacts are likely to play a significant role in adoption decisions regardless of whether they are based on objective data or not. This suggests that the following questions regarding user perceptions and system assessment will be central to efforts to develop meaningful evaluations of RNSs.

• —What is a good collaboration decision? What are near-, medium- and long-term outcomes variables? Are individuals’ perceptions of desirable collaborative relationships consistent with those found in empirical studies [ Cummings and Kiesler 2008 ]?
• —How do individual users determine if an RNS is useful? What forms of evidence do they use to assess whether a networking system has significantly contributed to their efforts to form and maintain a collaborative relationship?
• —What indicators do users rely on to assess whether an RNS has enough participants to be worthwhile as a source of potential collaborators (i.e., critical mass)? How do users determine whether it is beneficial for them to maintain their profile in an RNS?
• —How do individuals assess the costs and benefits of using an RNS? What prior experiences provide the basis for expected costs and benefits? What features and outcomes are most salient in development of users’ overall assessment of the system?

Evaluation of RNSs must assess their impact on organizational and societal outcomes.

RNSs are infrastructure systems that can only prove their value through the effects they have on their users, the community and/or organization, and the scientific field(s) in which they are used. This raises question of who should invest in these systems and who will derive value from this investment.

The decision makers with the authority to allocate resources for development and maintenance of an RNS are typically not its target users. As a result, their view of the value and cost of an RNS is rarely the same as, or even consistent with, that of the individual users of the system. Where each user may consider the time and effort to maintain their profile a significant cost, an administrator may only see the cost of additional personnel needed to gather the information from external systems (treating researchers’ time as “free”). While a researcher might consider the system useful if it allows her to maintain her general awareness of activities taking place in her social network, a funder may seek more quantifiable outcomes such as cost reduction or increased volume of publications. Therefore, although user perceptions of RNSs are critical for its success, evaluation of the organizational- and societal-level impacts is also necessary for their success as sustainable infrastructure systems.

While RNSs and the associated collaborative relationships can be beneficial for researchers and institutions, they can also be costly. Katz and Martin [1997] describe the money, time, and increased administrative effort required to support cross-institutional collaborations. These costs must also be considered when assessing RNS impacts. Together these issues suggest the following questions regarding larger-scale outcomes of implementing RNSs.

• —How can the benefits of RNS deployment be quantified? Will there be significant cost reductions for organizations that implement RNSs or do they just shift work from one part of the organization to another? How can the outcomes of supporting collaboration formation be measured?
• —What is the appropriate timeframe for evaluation of RNSs? Is it reasonable to expect impacts of RNS use to be visible in months, years, or decades?
• —What is the relationship between RNS use and organizationally significant impact measures? Which outcomes supported by RNSs, such as increased research productivity and innovative projects, are most likely to result in significant cost reductions?
• —Under what conditions will introduction of RNSs have the greatest impact? What disciplines, areas, and populations will be most affected by the availability of RNSs?

4. CONCLUSION

Choosing appropriate collaborators in science is important and likely to become more so. As this review has shown, the HCI and CSCW literatures provide important background knowledge and foundational concepts for research on RNSs. Beyond core areas such as expertise location systems and virtual communities, advancing our knowledge of research networking must also draw on knowledge representation, ontologies/controlled terminologies, human-computer interaction, social network formation, social matching, and the semantic Web. Moving RNSs forward requires a broad but integrated research program.

Given the current state of RNS development, a rapid, iterative cycle between foundational research, design, implementation, and evaluation seems desirable. The major funding agencies for biomedical (NIH) and basic science (NSF) research in the U.S. are keenly interested in a rapid reengineering of the research enterprise towards a more collaborative approach [ Cummings et al. 2008 ]. CSCW and HCI are disciplines that can add tremendous value to this transformation.

A primary goal of this article is to stimulate the HCI, CSCW, and related communities to consider studying research networking systems. As such, we view our work as a starting point to motivate a much more expansive discussion of research networking systems, and the pursuit of a broad and comprehensive research agenda.

ACKNOWLEDGMENTS

We appreciate all reviewers’ thorough and thoughtful comments and suggestions, Ellen Detlefsen’s input, Janine Carlock’s copy edits, and Michael Dziabiak’s help with formatting and submission.

This project was, in part, supported by a grant (1 U54 RR023506-01) from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH) and NIH Roadmap for Medical Research. Addition funding was provided by the National Science Foundation (OCI-0951630).

Various parts of this article are built on discussions and findings in Spallek et al. [2008] and Schleyer et al. [2008a ; 2008b ].

1 See http://www.vivoweb.org

2 See http://www.kmdedge.org

3 http://www.vivoweb.org/

4 http://connects.catalyst.harvard.edu/Profiles/search

5 http://www.icts.uiowa.edu/Loki/

6 http://www.direct2experts.org

7 http://www.direct2experts.org

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Diabetes Australia

Research Strategy Consultation

Diabetes Australia is committed to transforming the landscape of diabetes prevention, care and treatment through fostering evidence-based innovation and solutions. As we work toward a world free from diabetes, we invite people with lived experience, stakeholders, researchers, clinicians and advocates to come together to shape the future of diabetes research.

In this consultation paper , we seek your views on the development of Diabetes Australia’s research vision, purpose, strategic goals and approach. We highlight the progress made to-date, and our agenda to invest in research that is meaningful and delivers high impact to those who matter most – at least 1.5 million Australians living with diabetes.

This consultation will be open until 5:00pm Friday, 27 September 2024 (AEST).

Participating in the consultation

To participate in this consultation and provide feedback on the research strategy:

• Download, read and consider the consultation paper .
• Complete the consultation survey by 5:00pm Friday, 27 September 2024 (AEST).
• Contact [email protected] for further information or if you have questions.

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Office of the Vice President for Research

UI launches Implementation Science Center to translate research into practice

A new Board of Regents-approved University of Iowa Implementation Science Center (ISC) will help ensure that research findings are translated into real-world practices that benefit people as quickly and efficiently as possible. 

“Unfortunately, on average, it takes 17 years for research to be incorporated into routine practice in hospital or community settings,” said Heather Schacht Reisinger, professor of internal medicine in the Carver College of Medicine, and director of the new center. “The emerging field of implementation science seeks to close that gap.” 

Implementation science explores the organizational and behavioral factors that are important for ensuring that evidence-based practices reach all who could benefit as quickly as possible. The process involves engagement with key partners and communities to identify the best methods for integrating research findings in a way that impacts the overall health of a community. 

“As scientists, we are trained to communicate to other scientists through publications or conferences,” said Reisinger. “Implementation sciences takes this work a step further, ensuring that it doesn’t stay on the shelf. By also studying people, organizations, and systems, we can more effectively and efficiently help them integrate evidence-based practices into what they are already doing.” 

Building on the UI's strengths

Reisinger is a medical anthropologist with a depth of experience in the field of implementation science. She was a co-author of National Cancer Institute’s Qualitative Methods in Implementation Science white paper; is an Associate Editor for Implementation Science , the international flagship journal in the field; and presented at China’s first implementation science conference. 

The new center, which reports to the Office of the Vice President for Research with support from the Provost Office, Carver College of Medicine, Holden Comprehensive Cancer Center, College of Public Health, and College of Nursing, will serve as convening hub for scientists and practitioners from the health science colleges and beyond. 

“I’m excited to leverage my expertise and work with my colleagues across campus to build our capacity to conduct implementation science and contribute to a growing body of knowledge about what strategies and tools are most effective at moving research into practice,” said Reisinger.  In the coming months, Reisinger will assemble an advisory committee to help shape the center’s structure and strategic plan. The ultimate goal is to create new paradigms in the field and incorporate implementation science into researchers’ existing projects. 

“This work closely aligns with the goals outlined in the  University of Iowa Strategic Plan , including to expand the university’s impact on local and regional communities, the state of Iowa, and the world by leveraging transformational research and discovery,” said Lois Geist, interim vice president for research and associate provost for faculty.

Training in the application of evidence-based practices is not new to the UI. The College of Nursing has a 30-year tradition of mentoring and training nurses in evidence-based practice through the Iowa Model . “We have the opportunity to learn from and build on this model within the field of implementation science to create a pathway for practitioners across healthcare,” said Reisinger. 

The UI is the only site in the state with a College of Public Health and a comprehensive basic-to-clinical research enterprise situated within an academic medical center. The new Implementation Science Center will establish and expand partnerships with a number of institutional units, including the Institute for Clinical and Translational Science, which works with Professor Reisinger to successfully integrate implementation science into their programs. She serves as the unit’s associate director of engagement, integration, and implementation.

Rural connections

Supporting the integration of evidence-based practice in rural communities is a key area of focus for the center. While implementation science is rapidly expanding, less attention has been paid to the uptake of evidence-based practices and the latest research in rural health settings.

“Iowa is well-situated to contribute to expanding implementation science into rural health due to our geographic location and the rural focus of many of the centers and institutes on campus, such as the Prevention Research Center for Rural Health and Rural Policy Research Institute,” said Reisinger.

The ISC is already working with the P3-funded project “ Reducing the Impact of Lung Cancer among Iowans through Prevention and Early Detection ,” to explore questions of rural implementation. The project involves faculty from College of Public Health and Carver College of Medicine. “We are reaching out to counties with higher lung cancer rates to work with healthcare systems to support implementation of successful lung cancer screening, as well as increasing access to radon testing and reviewing local tobacco retailer marketing for points of intervention,” said Reisinger. “Our goal is to have local impact in Iowa, while improving our understanding of implementation strategies that are most effective in rural settings.”  

Get involved

Researchers who are interested in implementation science are invited to utilize the center’s available collaboration space and quiet workspaces on the second floor of the Medical Research Facility.   “Although we have a physical location in the MRF, we envision moving around campus to host trainings, lectures, and networking events in an effort to meet researchers where they are,” said Reisinger.  

On October 3 at 2:00 p.m. in the Nursing Clinical Education Center (W417 General Hospital), the center will host its first lecture of the semester, “ Building a Big Tent for Implementation Science Together: Reflections and Opportunities ” by David Chambers , deputy director for implementation science, Division of Cancer Control and Population Sciences, National Cancer Institute. After his lecture, Dr. Chambers will spend time with attendees who are interested in implementation science in cancer prevention and treatment to discuss funding opportunities at NCI. Virtual attendance will be available.

An implementation science journal club, where researchers discuss the latest research and their own works-in-progress, will be held on the third Thursday of the month at 1:00 pm in the Institute for Clinical and Translational Science (C44-A General Hospital).  The journal club is held as a hybrid meeting. Additional details about the center’s activities will be posted at isc.research.uiowa.edu . Join the listserv and monthly newsletter by contacting [email protected] .

Photo credit: Acacia Lab, Southern Medical University, Ghangzhou, China

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• Symposium on 20 years of the Research Centre

Symposium on 20 years of the Research Centre , Date: 2024.09.19 , format: Report , area: Authority

In May 2025, the Research Centre of the Federal Office for Migration and Refugees ( BAMF -FZ) celebrates its 20th anniversary and therefore invites to an international, scientific conference that will be held on 21 and 22 May 2025 in Nuremberg. The latest topics in migration and integration research will be discussed, such as the recruitment of skilled labour or the social participation of refugees. Ahead of the international conference, another conference will be held specifically dedicated to the situation of people who are obliged to leave the country on 20 and 21 May 2025. We cordially invite researchers to participate in the events with their contributions. The closing date for submissions is 30 November 2024.

In 2005, the Immigration Act mandated the Federal Office to conduct scientific research on migration and integration. This laid the foundation for today's Migration, Integration and Asylum Research Centre at the Federal Office. More than 50 employees currently work at the BAMF -FZ, which includes a Research Data Centre ( BAMF - FDZ ) since 2021.

"We can look back on 20 exciting and insightful years of migration and integration research. In recent years, these topics have become particularly important for Germany. For example, we are facing rising numbers of migrations to Germany as a consequence of international wars and crises. Furthermore, the shortage of skilled labour as a result of demographic change and educational expansion is becoming more noticeable.

Our research responds to various challenges in these areas and provides findings that policymakers can utilise in dealing with them. Wherever possible, we seek dialogue and network with scientists from other research institutions in order to broaden our perspectives and join forces. Our international conferences in May 2025, which I am very much looking forward to, will also provide such a networking opportunity."

Call for Papers - submit your contribution now!

For the international conference "analyse. evaluate. inform - Conference on 20 years of the BAMF Research Centre" on 21 and 22 May 2025, we invite you to submit contributions on the following topics:

• Immigration of skilled labour to Germany
• Living situation of refugees in Germany
• Innovative methods of migration and integration research

The deadline for submission is 30 November 2024, further information on the submission process can be found in the call for papers .

Contributions will be selected by 31 January 2025 and information on the registration will be made available in spring 2025.

Regardless of the above-mentioned conference, we would also like to draw your attention to the call for papers for the closing conference of the research project "Feasibility Study on the Immigration/Mobility of Persons Required to Leave Germany ( MIMAP )" entitled "Irregularly staying Migrants: Empirical Findings and Methodological Advances" on 20 and 21 May 2025. Contributions can address the topic from various thematic and methodological perspectives. The deadline for submissions is also 30 November 2024. Further information can be found in the call for papers.

Further information

• Call for Papers: International conference on "20 years of the BAMF Research Centre" on "analyse. evaluate. inform"
• International Conference on "20 Years Research Centre at the BAMF": Submission of abstracts pdf, 102KB, accessible
• Call for Papers for the MIMAP closing conference
• MIMAP-Abschlusskonferenz: Formular zur Beitragseinreichung / MIMAP closing conference: Submission form pdf, 104KB, accessible
• Flyer on "Migration Research"