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Approximation of Nash equilibria and the network community structure detection problem

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  • Suciu Mihai-Alexandru
  • Gaskó Noémi
  • Lung Rodica Ioana

Abstract

Game theory based methods designed to solve the problem of community structure detection in complex networks have emerged in recent years as an alternative to classical and optimization based approaches. The Mixed Nash Extremal Optimization uses a generative relation for the characterization of Nash equilibria to identify the community structure of a network by converting the problem into a non-cooperative game. This paper proposes a method to enhance this algorithm by reducing the number of payoff function evaluations. Numerical experiments performed on synthetic and real-world networks show that this approach is efficient, with results better or just as good as other state-of-the-art methods.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0174963
    DOI: 10.1371/journal.pone.0174963
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    References listed on IDEAS

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    1. 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.
    2. Supreet Mandala & Soundar Kumara & Kalyan Chatterjee, 2014. "A Game-Theoretic Approach to Graph Clustering," INFORMS Journal on Computing, INFORMS, vol. 26(3), pages 629-643, August.
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