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Introduction

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Potential-Based Analysis of Social, Communication, and Distributed Networks

Part of the book series: Springer Theses ((Springer Theses))

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Abstract

Social and distributed networks constitute an important research area which has attracted a lot of attention in the past few years.

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References

  1. J. N. Tsitsiklis, D. P. Bertsekas, and M. Athans, “Distributed asynchronous deterministic and stochastic gradient optimization algorithms,” IEEE Transactions on Automatic Control, vol. 31, no. 9, pp. 803–812, 1986.

    Article  MathSciNet  MATH  Google Scholar 

  2. A. Nedic and A. Ozdaglar, “Distributed subgradient methods in multi-agent optimization,” IEEE Transactions on Automatic Control, vol. 54, no. 1, pp. 48–61, 2009.

    Article  MATH  Google Scholar 

  3. C. Gao, J. Cortes, and F. Bullo, “Notes on averaging over acyclic digraphs and discrete coverage control,” Automatica, vol. 44, no. 8, pp. 2120–2127, 2008.

    Article  MathSciNet  MATH  Google Scholar 

  4. W. Ren and R. W. Beard, Distributed Consensus in Multi-Vehicle Cooperative Control. London: Springer, 2008.

    Book  MATH  Google Scholar 

  5. A. Olshevsky, “Efficient information aggregation strategies for distributed control and signal processing,” Ph. D. thesis, arXiv:1009.6036, 2010.

  6. M. Rabbat and R. D. Nowak, “Distributed optimization in sensor networks,” in Information Processing and Sensor Networks. IEEE, 2004, pp. 29–27.

    Google Scholar 

  7. L. Xiao, S. Boyd, and S.-J. Kim, “Distributed average consensus with least-mean-square deviation,” Journal of Parallel and Distributed Computing, vol. 67, no. 1, pp. 33–46, 2007.

    Article  MATH  Google Scholar 

  8. S. Ram, V. Veeravalli, and A. Nedic, “Distributed non-autonomous power control through distributed convex optimization,” in INFOCOM. IEEE, 2008, pp. 3001–3005.

    Google Scholar 

  9. B. Ghosh and S. Muthukrishnan, “Dynamic load balancing in parallel and distributed networks by random matchings,” in Proceedings of the Sixth Annual ACM Symposium on Parallel Algorithms and Architectures. ACM, 1994, pp. 226–235.

    Google Scholar 

  10. G. Neglia, G. Reina, and S. Alouf, “Distributed gradient optimization for epidemic routing: a preliminar evaluation,” in Wireless Days, 2nd IFIP. IEEE, 2009, pp. 1–6.

    Google Scholar 

  11. D. P. Bertsekas and J. N. Tsitsiklis, Parallel and Distributed Computation. Old Tappan, NJ (USA); Prentice Hall Inc, 1989.

    Google Scholar 

  12. L. Xiao and S. Boyd, “Fast linear iterations for distributed averaging,” Systems and Control Letters, p. 6517, 2004.

    Google Scholar 

  13. S. Boyd, A. Ghosh, B. Prabhakar, and D. Shah, “Gossip algorithms: Design, analysis and applications,” in Proceedings IEEE INFOCOM 2005. 24th Annual Joint Conference of the IEEE Computer and Communications Societies, vol. 3. IEEE, 2005, pp. 1653–1664.

    Google Scholar 

  14. M. Draief and M. Vojnovic, “Convergence speed of binary interval consensus,” SIAM Journal on Control and Optimization, vol. 50, no. 3, pp. 1087–1109, 2012.

    Article  MathSciNet  MATH  Google Scholar 

  15. A. Kashyap, T. Başar, and R. Srikant, “Quantized consensus,” Automatica, vol. 43, no. 7, pp. 1192–1203, 2007.

    Article  MathSciNet  MATH  Google Scholar 

  16. S. Etesami and T. Başar, “Convergence time for unbiased quantized consensus,” Proc. 52nd IEEE Conference on Decision and Control. CDC’13, Dec 10-13, 2013; Florence, Italy, pp. 6190–6195, 2013.

    Google Scholar 

  17. R. Carli, F. Fagnani, P. Frasca, and S. Zampieri, “Gossip consensus algorithms via quantized communication,” Automatica, vol. 46, no. 1, pp. 70–80, 2010.

    Article  MathSciNet  MATH  Google Scholar 

  18. M. Zhu and S. Martinez, “On the convergence time of asynchronous distributed quantized averaging algorithms,” IEEE Transactions on Automatic Control, vol. 56, no. 2, pp. 386–390, 2011.

    Article  MathSciNet  Google Scholar 

  19. K. Cai and H. Ishii, “Quantized consensus and averaging on gossip digraphs,” IEEE Transactions on Automatic Control, vol. 56, no. 9, pp. 2087–2100, 2011.

    Article  MathSciNet  Google Scholar 

  20. F. Bénézit, P. Thiran, and M. Vetterli, “Interval consensus: from quantized gossip to voting,” in IEEE International Conference on Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE, 2009, pp. 3661–3664.

    Google Scholar 

  21. R. Olfati-Saber and R. M. Murray, “Consensus problems in networks of agents with switching topology and time-delays,” IEEE Transactions on Automatic Control, vol. 49, no. 9, pp. 1520–1533, 2004.

    Article  MathSciNet  Google Scholar 

  22. S. Shang, P. Cuff, P. Hui, and S. Kulkarni, “An upper bound on the convergence time for quantized consensus,” in 2013 Proceedings INFOCOM. IEEE, 2013, pp. 600–604.

    Google Scholar 

  23. K. Cai and H. Ishii, “Convergence time analysis of quantized gossip consensus on digraphs,” Automatica, vol. 48, pp. 2344–2351, 2012.

    Article  MathSciNet  MATH  Google Scholar 

  24. R. Carli, F. Bullo, and S. Zampieri, “Quantized average consensus via dynamic coding/decoding schemes,” International Journal of Robust and Nonlinear Control, vol. 20, no. 2, pp. 156–175, 2010.

    Article  MathSciNet  MATH  Google Scholar 

  25. T. Li and L. Xie, “Distributed coordination of multi-agent systems with quantized-observer based encoding-decoding,” IEEE Transactions on Automatic Control, vol. 57, no. 12, pp. 3023–3027, 2012.

    Article  MathSciNet  Google Scholar 

  26. M. Guo and D. Dimarogonas, “Consensus with quantized relative state measurements,” Automatica, vol. 49, no. 8, pp. 2531–2537, 2013.

    Article  MathSciNet  Google Scholar 

  27. J. Lavaei and R. M. Murray, “Quantized consensus by means of gossip algorithm,” IEEE Transactions on Automatic Control, vol. 57, no. 1, pp. 19–32, 2012.

    Article  MathSciNet  Google Scholar 

  28. A. Olshevsky, “Consensus with ternary messages,” SIAM Journal on Control and Optimization, vol. 52, no. 2, pp. 987–1009, 2014.

    Article  MathSciNet  MATH  Google Scholar 

  29. M. El Chamie, J. Liu, and T. Başar, “Design and analysis of distributed averaging with quantized communication,” Proc. 53rd IEEE Conference on Decision and Control (CDC’14, Dec 15-17, 2014; Los Angeles, CA), pp. 3860–3865, 2014.

    Google Scholar 

  30. J. M. Hendrickx, A. Olshevsky, and J. N. Tsitsiklis, “Distributed anonymous discrete function computation,” IEEE Transactions on Automatic Control, vol. 56, no. 10, pp. 2276–2289, 2011.

    Article  MathSciNet  Google Scholar 

  31. S. R. Etesami and T. Başar, “Convergence time for unbiased quantized consensus over static and dynamic networks,” IEEE Transactions on Automatic Control, 61(2), pp. 443-455, February, 2016.

    MathSciNet  Google Scholar 

  32. T. C. Aysal, A. D. Sarwate, and A. G. Dimakis, “Reaching consensus in wireless networks with probabilistic broadcast,” Proceedings of the 47th Annual Allerton Conference on Communication, Control, and Computing, pp. 732–739, 2009.

    Google Scholar 

  33. S. Sundaran and C. N. Hadjicostis, “Finite-time distributed consensus in graphs with time-invariant topologies,” Proc. American Control Conference, pp. 711–716, 2007.

    Google Scholar 

  34. M. H. DeGroot, “Reaching a consensus,” Journal of the American Statistical Association, vol. 69, no. 345, pp. 118–121, 1974.

    Article  MATH  Google Scholar 

  35. A. Olshevsky and J. Tsitsiklis, “On the nonexistence of quadratic Lyapunov functions for consensus algorithms,” IEEE Transactions on Automatic Control, vol. 53, pp. 2642–2645, 2008.

    Article  MathSciNet  Google Scholar 

  36. J. C. Delvenne, R. Carli, and S. Zampieri, “Optimal strategies in the average consensus problem,” Wavelet Analysis and Multiresolution Methods, vol. 56, pp. 759–765, 2009.

    MathSciNet  MATH  Google Scholar 

  37. L.Wang and F. Xiao, “Finite-time consensus problems for networks of dynamic agents,” IEEE Transaction On Automatic Control, vol. 55, no. 5, 2010.

    Google Scholar 

  38. N. E. Friedkin and E. C. Johnsen, “Social influence networks and opinion change,” Advances in Group Processes, vol. 16, no. 1, pp. 1–29, 1999.

    Google Scholar 

  39. R. Hegselmann and U. Krause, “Opinion dynamics and bounded confidence models, analysis, and simulation,” Artificial Societies and Social Simulation, vol. 5, pp. 1–33, 2002.

    Google Scholar 

  40. J. B. Stiff and P. A. Mongeau, Persuasive Communication. Guilford Press, 2003.

    Google Scholar 

  41. N. E. Friedkin and E. C. Johnsen, Social Influence Network Theory. Cambridge University Press, New York, 2011.

    Book  MATH  Google Scholar 

  42. F. Bullo, J. Cortes, and S. Martinez, Distributed Control of Robotic Networks. Princeton University Press, 2009.

    Google Scholar 

  43. J. Lorenz, Repeated averaging and bounded-confidence, modeling, analysis and simulation of continuous opinion dynamics. Ph.D. Dissertation, University of Bremen, 2007.

    Google Scholar 

  44. J. Lorenz, “Heterogeneous bounds of confidence: Meet, discuss and find consensus!” Complexity, vol. 15, no. 4, pp. 43–52, 2010.

    MathSciNet  Google Scholar 

  45. A. Mirtabatabaei and F. Bullo, “Opinion dynamics in heterogeneous networks: Convergence conjectures and theorems,” SIAM Journal on Control and Optimization, vol. 50, no. 5, pp. 2763–2785, 2012.

    Article  MathSciNet  MATH  Google Scholar 

  46. S. R. Etesami, T. Başar, A. Nedić, and B. Touri, “Termination time of multidimensional Hegselmann-Krause opinion dynamics,” in Proc. American Control Conference (ACC). IEEE, 2013, pp. 1255–1260.

    Google Scholar 

  47. A. Bhattacharyya, M. Braverman, B. Chazelle, and H. L. Nguyen, “On the convergence of the Hegselmann-Krause system,” in Proceedings of the 4th Conference on Innovations in Theoretical Computer Science. ACM, 2013, pp. 61–66.

    Google Scholar 

  48. B. Chazelle, “The total s-energy of a multiagent system,” SIAM Journal on Control and Optimization, vol. 49, no. 4, pp. 1680–1706, 2011.

    Article  MathSciNet  MATH  Google Scholar 

  49. S. Boyd, A. Ghosh, B. Prabhakar, and D. Shah, “Gossip algorithms: Design, analysis, and applications,” IEEE INFOCOM, vol. 3, pp. 1653–1664, 2005.

    Google Scholar 

  50. S. Mohajer and B. Touri, “On convergence rate of scalar Hegselmann-Krause dynamics,” in Proc. American Control Conference (ACC). IEEE, 2013, pp. 206–210.

    Google Scholar 

  51. A. Nedić and B. Touri, “Multi-dimensional Hegselmann-Krause dynamics,” in Proc. 2012 IEEE 51st Annual Conference on Decision and Control (CDC). IEEE, 2012, pp. 68–73.

    Google Scholar 

  52. B. Touri and C. Langbort, “On endogenous random consensus and averaging dynamics,” Proc. 52nd IEEE Conference on Decision and Control (CDC), pp. 6208–6212, 2013.

    Google Scholar 

  53. J. R. Marden, G. Arslan, and J. S. Shamma, “Cooperative control and potential games,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 39, no. 6, pp. 1393–1407, 2009.

    Article  Google Scholar 

  54. A. Rantzer, “Using game theory for distributed control engineering,” Language, vol. 280, no. 53, p. 16, 2008.

    Google Scholar 

  55. H. Zhang, F. L. Lewis, and Z. Qu, “Lyapunov, adaptive, and optimal design techniques for cooperative systems on directed communication graphs,” IEEE Transactions on Industrial Electronics, vol. 59, no. 7, pp. 3026–3041, 2012.

    Article  Google Scholar 

  56. J. M. Hendrickx, Graphs and networks for the analysis of autonomous agent systems. Ph.D. dissertation, Université Catholique de Louvain, 2011.

    Google Scholar 

  57. V. Pacifici and G. Dan, “Convergence in player-specific graphical resource allocation games,” IEEE Journal on Selected Areas in Communications, vol. 30, no. 11, pp. 2190–2199, 2012.

    Article  Google Scholar 

  58. A. M. Masucci and A. Silva, “Strategic resource allocation for competitive influence in social networks,” arXiv preprint  arXiv:1402.5388, 2014.

  59. I. Milchtaich, “Congestion games with player-specific payoff functions,” Games and Economic Behavior, vol. 13, no. 1, pp. 111–124, 1996.

    Article  MathSciNet  MATH  Google Scholar 

  60. H. Ackermann, H. Röglin, and B. Vöcking, “On the impact of combinatorial structure on congestion games,” Journal of the ACM (JACM), vol. 55, no. 6, p. 25, 2008.

    Article  MathSciNet  MATH  Google Scholar 

  61. A. Fabrikant, C. Papadimitriou, and K. Talwar, “The complexity of pure Nash equilibria,” in Proceedings of the Thirty-Sixth Annual ACM Symposium on Theory of Computing. ACM, 2004, pp. 604–612.

    Google Scholar 

  62. V. Anantharam, “On the Nash dynamics of congestion games with player-specific utility,” in Proc. 43rd IEEE Conference on Decision and Control, 2004. CDC, vol. 5. IEEE, 2004, pp. 4673–4678.

    Google Scholar 

  63. I. Caragiannis, M. Flammini, C. Kaklamanis, P. Kanellopoulos, and L. Moscardelli, “Tight bounds for selfish and greedy load balancing,” in Automata, Languages and Programming. Springer, 2006, pp. 311–322.

    Google Scholar 

  64. G. G. Pollatos, O. A. Telelis, and V. Zissimopoulos, “On the social cost of distributed selfish content replication,” in Ad Hoc and Sensor Networks, Wireless Networks, Next Generation Internet. Springer, 2008, pp. 195–206.

    Google Scholar 

  65. E. J. Friedman, J. Y. Halpern, and I. Kash, “Efficiency and Nash equilibria in a scrip system for P2P networks,” in Proceedings of the 7th ACM Conference on Electronic Commerce. ACM, 2006, pp. 140–149.

    Google Scholar 

  66. N. Laoutaris, O. Telelis, V. Zissimopoulos, and I. Stavrakakis, “Distributed selfish replication,” IEEE Transactions on Parallel and Distributed Systems, vol. 17, no. 12, pp. 1401–1413, 2006.

    Article  Google Scholar 

  67. R. Gopalakrishnan, D. Kanoulas, N. N. Karuturi, C. P. Rangan, R. Rajaraman, and R. Sundaram, “Cache me if you can: capacitated selfish replication games,” in LATIN 2012: Theoretical Informatics. Springer, 2012, pp. 420–432.

    Google Scholar 

  68. R. T. Maheswaran and T. Başar, “Nash equilibrium and decentralized negotiation in auctioning divisible resources,” Group Decision and Negotiation, vol. 12, no. 5, pp. 361–395, 2003.

    Article  Google Scholar 

  69. R. T. Maheswaran and T. Başar, “Decentralized network resource allocation as a repeated noncooperative market game,” in Proceedings of the 40th IEEE Conference on Decision and Control, vol. 5. IEEE, 2001, pp. 4565–4570.

    Google Scholar 

  70. M. X. Goemans, L. Li, V. S. Mirrokni, and M. Thottan, “Market sharing games applied to content distribution in ad hoc networks,” IEEE Journal on Selected Areas in Communications, vol. 24, no. 5, pp. 1020–1033, 2006.

    Article  Google Scholar 

  71. B.-G. Chun, K. Chaudhuri, H. Wee, M. Barreno, C. H. Papadimitriou, and J. Kubiatowicz, “Selfish caching in distributed systems: a game-theoretic analysis,” in Proceedings of the Twenty-Third Annual ACM Symposium on Principles of Distributed Computing. ACM, 2004, pp. 21–30.

    Google Scholar 

  72. S. Goyal and F. Vega-Redondo, “Learning, network formation and coordination,” Tinbergen Institute, Tech. Rep., 2000.

    MATH  Google Scholar 

  73. R. Johari, S. Mannor, and J. N. Tsitsiklis, “Efficiency loss in a network resource allocation game: the case of elastic supply,” IEEE Transactions on Automatic Control, vol. 50, no. 11, pp. 1712–1724, 2005.

    Article  MathSciNet  Google Scholar 

  74. J. R. Marden and T. Roughgarden, “Generalized efficiency bounds in distributed resource allocation,” in Proc. 49th IEEE Conference on Decision and Control (CDC). IEEE, 2010, pp. 2233–2238.

    Google Scholar 

  75. E. Koutsoupias and C. Papadimitriou, “Worst-case equilibria,” in STACS 99. Springer, 1999, pp. 404–413.

    Google Scholar 

  76. A. Vetta, “Nash equilibria in competitive societies, with applications to facility location, traffic routing and auctions,” in Proceedings. 43rd Annual IEEE Symposium on Foundations of Computer Science. IEEE, 2002, pp. 416–425.

    Google Scholar 

  77. J. R. Correa, A. S. Schulz, and N. E. Stier-Moses, “Selfish routing in capacitated networks,” Mathematics of Operations Research, vol. 29, no. 4, pp. 961–976, 2004.

    Article  MathSciNet  MATH  Google Scholar 

  78. N. Nisan, T. Roughgarden, E. Tardos, and V. V. Vazirani, Algorithmic Game Theory. Cambridge University Press Cambridge, 2007, vol. 1.

    Google Scholar 

  79. S. Goyal and M. Kearns, “Competitive contagion in networks,” in Proceedings of the 44th Symposium on Theory of Computing. ACM, 2012, pp. 759–774.

    Google Scholar 

  80. H. P. Young, “The diffusion of innovations in social networks,” Economy as an Evolving Complex System. Proceedings Volume in the Santa Fe Institute Studies in the Sciences of Complexity, vol. 3, pp. 267–282, 2002.

    Google Scholar 

  81. D. Kempe, J. Kleinberg, and É. Tardos, “Maximizing the spread of influence through a social network,” in Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2003, pp. 137–146.

    Google Scholar 

  82. D. Acemoglu, A. Ozdaglar, and E. Yildiz, “Diffusion of innovations in social networks,” in Proc. 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC). IEEE, 2011, pp. 2329–2334.

    Google Scholar 

  83. A. Khanafer, T. Başar, and B. Gharesifard, “Stability properties of infected networks with low curing rates,” in Proc. 2014 American Control Conference (ACC 2014), Portland, OR. IEEE, 2014, pp. 3591–3596.

    Google Scholar 

  84. A. Khanafer and T. Başar, “Information spread in networks: control, games, and equilibria,” in Proc. Information Theory and Applications Workshop (ITA’14), San Diego, California, 2014.

    Google Scholar 

  85. S. Bharathi, D. Kempe, and M. Salek, “Competitive influence maximization in social networks,” in Internet and Network Economics. Springer, 2007, pp. 306–311.

    Google Scholar 

  86. S. Brânzei and K. Larson, “Social distance games,” in Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence-Volume One. AAAI Press, 2011, pp. 91–96.

    Google Scholar 

  87. M. Richardson and P. Domingos, “Mining knowledge-sharing sites for viral marketing,” in Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2002, pp. 61–70.

    Google Scholar 

  88. Y. Singer, “How to win friends and influence people, truthfully: influence maximization mechanisms for social networks,” in Proceedings of the Fifth ACM International Conference on Web Search and Data Mining. ACM, 2012, pp. 733–742.

    Google Scholar 

  89. N. Alon, M. Feldman, A. D. Procaccia, and M. Tennenholtz, “A note on competitive diffusion through social networks,” Information Processing Letters, vol. 110, no. 6, pp. 221–225, 2010.

    Article  MathSciNet  MATH  Google Scholar 

  90. R. Takehara, M. Hachimori, and M. Shigeno, “A comment on pure-strategy Nash equilibria in competitive diffusion games,” Information Processing Letters, vol. 112, no. 3, pp. 59–60, 2012.

    Article  MathSciNet  MATH  Google Scholar 

  91. E. Yildiz, D. Acemoglu, A. Ozdaglar, A. Saberi, and A. Scaglione, “Discrete opinion dynamics with stubborn agents,” Available at SSRN 1744113, 2011.

    Google Scholar 

  92. M. Fazli, M. Ghodsi, J. Habibi, P. J. Khalilabadi, V. Mirrokni, and S. S. Sadeghabad, “On the non-progressive spread of influence through social networks,” in LATIN 2012: Theoretical Informatics. Springer, 2012, pp. 315–326.

    Google Scholar 

  93. E. Ackerman, O. Ben-Zwi, and G. Wolfovitz, “Combinatorial model and bounds for target set selection,” Theoretical Computer Science, vol. 411, no. 44, pp. 4017–4022, 2010.

    Article  MathSciNet  MATH  Google Scholar 

  94. A. Gionis, E. Terzi, and P. Tsaparas, “Opinion maximization in social networks.” in SDM. SIAM, 2013, pp. 387–395.

    Google Scholar 

  95. L. Small and O. Mason, “Nash equilibria for competitive information diffusion on trees,” Information Processing Letters, vol. 113, no. 7, pp. 217–219, 2013.

    Article  MathSciNet  MATH  Google Scholar 

  96. L. Small and O. Mason, “Information diffusion on the iterated local transitivity model of online social networks,” Discrete Applied Mathematics, 2012.

    Google Scholar 

  97. D. Acemoglu, G. Como, F. Fagnani, and A. Ozdaglar, “Opinion fluctuations and disagreement in social networks,” Mathematics of Operations Research, vol. 38, no. 1, pp. 1–27, 2013.

    Article  MathSciNet  MATH  Google Scholar 

  98. D. Acemoglu, G. Como, F. Fagnani, and A. Ozdaglar, “Message passing optimization of harmonic influence centrality,” ACM SIGMETRICS Performance Evaluation Review, vol. 42(3), 2014.

    Google Scholar 

  99. L. Vassio, F. Fagnani, P. Frasca, and A. Ozdaglar, “Harmonic influence in large-scale networks,” IEEE Transactions on Control of Network Systems, vol. 1(1), 2014.

    Google Scholar 

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    Seyed Rasoul Etesami

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Etesami, S.R. (2017). Introduction. In: Potential-Based Analysis of Social, Communication, and Distributed Networks. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-319-54289-8_1

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