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Game Theory and Extremal Optimization for Community Detection in Complex Dynamic Networks

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  • Rodica Ioana Lung
  • Camelia Chira
  • Anca Andreica

Abstract

The detection of evolving communities in dynamic complex networks is a challenging problem that recently received attention from the research community. Dynamics clearly add another complexity dimension to the difficult task of community detection. Methods should be able to detect changes in the network structure and produce a set of community structures corresponding to different timestamps and reflecting the evolution in time of network data. We propose a novel approach based on game theory elements and extremal optimization to address dynamic communities detection. Thus, the problem is formulated as a mathematical game in which nodes take the role of players that seek to choose a community that maximizes their profit viewed as a fitness function. Numerical results obtained for both synthetic and real-world networks illustrate the competitive performance of this game theoretical approach.

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  • Rodica Ioana Lung & Camelia Chira & Anca Andreica, 2014. "Game Theory and Extremal Optimization for Community Detection in Complex Dynamic Networks," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-11, February.
    • Handle: RePEc:plo:pone00:0086891
    DOI: 10.1371/journal.pone.0086891
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    1. Suciu Mihai-Alexandru & Gaskó Noémi & Lung Rodica Ioana, 2017. "Approximation of Nash equilibria and the network community structure detection problem," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-24, May.

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