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Deterministic modularity optimization

Author

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  • S. Lehmann
  • L. K. Hansen

Abstract

We study community structure of networks. We have developed a scheme for maximizing the modularity Q [Newman and Girvan, Phys. Rev. E 69, 026113 (2004)] based on mean field methods. Further, we have defined a simple family of random networks with community structure; we understand the behavior of these networks analytically. Using these networks, we show how the mean field methods display better performance than previously known deterministic methods for optimization of Q. Copyright EDP Sciences/Società Italiana di Fisica/Springer-Verlag 2007

Suggested Citation

  • S. Lehmann & L. K. Hansen, 2007. "Deterministic modularity optimization," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 60(1), pages 83-88, November.
    • Handle: RePEc:spr:eurphb:v:60:y:2007:i:1:p:83-88
    DOI: 10.1140/epjb/e2007-00313-2
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    Cited by:

    1. Xiang, Ju & Tang, Yan-Ni & Gao, Yuan-Yuan & Zhang, Yan & Deng, Ke & Xu, Xiao-Ke & Hu, Ke, 2015. "Multi-resolution community detection based on generalized self-loop rescaling strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 432(C), pages 127-139.
    2. Jiang, Yasong & Li, Taisong & Zhang, Yan & Yan, Yonghong, 2017. "Collective prediction based on community structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 587-598.
    3. Leo Liberti, 2020. "Distance geometry and data science," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(2), pages 271-339, July.

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