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Identifying community structures in dynamic networks

  • Hamidreza Alvari
  • Alireza Hajibagheri
  • Gita Sukthankar
  • Kiran Lakkaraju
Original Article

Abstract

Most real-world social networks are inherently dynamic, composed of communities that are constantly changing in membership. To track these evolving communities, we need dynamic community detection techniques. This article evaluates the performance of a set of game-theoretic approaches for identifying communities in dynamic networks. Our method, D-GT (Dynamic Game-Theoretic community detection), models each network node as a rational agent who periodically plays a community membership game with its neighbors. During game play, nodes seek to maximize their local utility by joining or leaving the communities of network neighbors. The community structure emerges after the game reaches a Nash equilibrium. Compared to the benchmark community detection methods, D-GT more accurately predicts the number of communities and finds community assignments with a higher normalized mutual information, while retaining a good modularity.

Keywords

Community detection Dynamic social networks Game-theoretic models 

Notes

Acknowledgments

We would like to thank Drs. Nitin Agarwal and Rolf T. Wigand at University of Arkansas for providing the Travian dataset.

References

  1. Adjeroh D, Kandaswamy U (2007) Game-theoretic analysis of network community structure. Int J Comput Intell Res 3(4):313–325Google Scholar
  2. Alvari H, Hashemi S, Hamzeh A (2011) Detecting overlapping communities in social networks by game theory and structural equivalence concept. In: Artificial intelligence and computational intelligence, Springer, Berlin, pp 620–630Google Scholar
  3. Alvari H, Hashemi S, Hamzeh A (2013) Discovering overlapping communities in social networks: a novel game-theoretic approach. AI Commun 36(2):161–177MathSciNetzbMATHGoogle Scholar
  4. Alvari H, Hajibagheri A, Sukthankar G (2014a) Community detection in dynamic social networks: a game-theoretic approach. In: IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM), pp 101–107Google Scholar
  5. Alvari H, Lakkaraju K, Sukthankar G, Whetzel J (2014) Predicting guild membership in massively multiplayer online games. In: Kennedy W, Agarwal N, Yang S (eds) Social computing, behavioral-cultural modeling and prediction, vol 8393., Lecture notes in computer science Springer, New York, pp 215–222CrossRefGoogle Scholar
  6. Beigi G, Jalili M, Alvari H, Sukthankar G (2014) Leveraging community detection for accurate trust prediction. In: ASE international conference on social computingGoogle Scholar
  7. Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech Theory Exp 2008(10):P10008CrossRefGoogle Scholar
  8. Cazabet R, Amblard F, Hanachi C (2010) Detection of overlapping communities in dynamical social networks. In: IEEE international conference on social computing, pp 309–314Google Scholar
  9. Chen W, Liu Z, Sun X, Wang Y (2010) A game-theoretic framework to identify overlapping communities in social networks. Data Min Knowl Discov 21(2):224–240MathSciNetCrossRefGoogle Scholar
  10. Danon L, Diaz-Guilera A, Duch J, Arenas A (2005) Comparing community structure identification. J Stat Mech Theory Exp 2005(09):P09008CrossRefGoogle Scholar
  11. Folino F, Pizzuti C (2014) An evolutionary multiobjective approach for community discovery in dynamic networks. Knowl Data Eng IEEE Trans 26(8):1838–1852CrossRefGoogle Scholar
  12. Fortunato S (2010) Community detection in graphs. Phys Rep 486(3):75–174MathSciNetCrossRefGoogle Scholar
  13. Fortunato S, Barthelemy M (2007) Resolution limit in community detection. Proc Natl Acad Sci 104(1):36–41CrossRefGoogle Scholar
  14. Girvan M, Newman MEJ (2002) Community structure in social and biological networks. Proc Natl Acad Sci 99(12):7821–7826MathSciNetCrossRefzbMATHGoogle Scholar
  15. Hajibagheri A, Alvari H, Hamzeh A, Hashemi S (2012) Community detection in social networks using information diffusion. In: IEEE/ACM international conference on advances in social networks analysis and mining, pp 702–703Google Scholar
  16. Hajibagheri A, Hamzeh A, Sukthankar G (2013) Modeling information diffusion and community membership using stochastic optimization. In: IEEE/ACM international conference on advances in social networks analysis and mining, pp 175–182Google Scholar
  17. Hajibagheri A, Lakkaraju K, Sukthankar G, Wigand RT, Agarwal N (2015) Conflict and communication in massively-multiplayer online games. In: Social computing, behavioral-cultural modeling, and prediction, Springer, Berlin, pp 65–74Google Scholar
  18. Hopcroft J, Khan O, Kulis B, Selman B (2004) Tracking evolving communities in large linked networks. Proc Natl Acad Sci 101:5249–5253CrossRefGoogle Scholar
  19. Hui P, Yoneki E, Chan SY, Crowcroft J (2007) Distributed community detection in delay tolerant networks. In: Proceedings of ACM/IEEE international workshop on mobility in the evolving internet architecture, p 7Google Scholar
  20. Lancichinetti A, Radicchi F, Ramasco JJ, Fortunato S et al (2011) Finding statistically significant communities in networks. PloS One 6(4):e18961CrossRefGoogle Scholar
  21. Leicht EA, Newman ME (2008) Community structure in directed networks. Phys Rev Lett 100(11):118703CrossRefGoogle Scholar
  22. Leskovec J, Kleinberg J, Faloutsos C (2005) Graphs over time: densification laws, shrinking diameters and possible explanations. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery in data mining, ACM, pp 177–187Google Scholar
  23. Lin Y-R, Chi Y, Zhu S, Sundaram H, Tseng BL (2008) Facetnet: a framework for analyzing communities and their evolutions in dynamic networks. In: Proceeding of the international conference on the world wide web, ACM, pp 685–694Google Scholar
  24. MacKay DJ (2003) Information theory, inference and learning algorithms. Cambridge University Press, CambridgezbMATHGoogle Scholar
  25. Newman ME (2006) Modularity and community structure in networks. Proc Natl Acad Sci 103(23):8577–8582CrossRefGoogle Scholar
  26. Newman MEJ (2004) Fast algorithm for detecting community structure in networks. Phys Rev E 69(6):066133CrossRefGoogle Scholar
  27. Nguyen NP, Dinh TN, Shen Y, Thai MT (2014) Dynamic social community detection and its applications. PLoS One 9(4):e91431, 04Google Scholar
  28. Nisan N, Roughgarden T, Tardos E, Vazirani VV (eds) (2007) Algorithmic game theory. Cambridge University Press, CambridgeGoogle Scholar
  29. Palla G, Pollner P, Barabási A-L, Vicsek T (2009) Social group dynamics in networks. In: Adaptive networks, Springer, Berlin, pp 11–38Google Scholar
  30. Raghavan UN, Albert R, Kumara S (2007) Near linear time algorithm to detect community structures in large-scale networks. Phys Rev E 76(3)Google Scholar
  31. Rosvall M, Bergstrom CT (2008) Maps of random walks on complex networks reveal community structure. Proc Natl Acad Sci 105(4):1118–1123CrossRefGoogle Scholar
  32. Satuluri V, Parthasarathy S (2009) Scalable graph clustering using stochastic flows: applications to community discovery. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining, pp 737–746Google Scholar
  33. Sun J, Faloutsos C, Papadimitriou S, Yu PS (2007) Graphscope: parameter-free mining of large time-evolving graphs. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining, pp 687–696Google Scholar
  34. Takaffoli M, Rabbany R, Zaïane OR (2014) Community evolution prediction in dynamic social networks. In: IEEE/ACM international conference on advances in social networks analysis and mining, pp 9–16Google Scholar
  35. Van Dongen S (2000) A cluster algorithm for graphs. Technical report 10, Centrum voor Wiskunde en InformaticaGoogle Scholar
  36. Xie J, Szymanski B (2012) Towards linear time overlapping community detection in social networks. In: Pacific-Asia conference on knowledge discovery and data mining, LNAI, Springer, BerlinGoogle Scholar
  37. Xie J, Szymanski BK (2011) Community detection using a neighborhood strength driven label propagation algorithm. In: Network science workshop (NSW), IEEE, pp 188–195Google Scholar
  38. Xie J, Chen M, Szymanski BK (2013) LabelrankT: incremental community detection in dynamic networks via label propagation. arXiv preprint arXiv:1305.2006
  39. Zhang Y, Wang J, Wang Y, Zhou L (2009) Parallel community detection on large networks with propinquity dynamics. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining, New York, NY, pp 997–1006Google Scholar

Copyright information

© Springer-Verlag Wien 2016

Authors and Affiliations

  • Hamidreza Alvari
    • 1
  • Alireza Hajibagheri
    • 1
  • Gita Sukthankar
    • 1
  • Kiran Lakkaraju
    • 2
  1. 1.University of Central FloridaOrlandoUSA
  2. 2.Sandia National LabsAlbuquerqueUSA

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