Identifying community structures in dynamic networks

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


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.


Community detection Dynamic social networks Game-theoretic models 



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


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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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