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Mixture models with entropy regularization for community detection in networks

Author

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  • Chang, Zhenhai
  • Yin, Xianjun
  • Jia, Caiyan
  • Wang, Xiaoyang

Abstract

Community detection is a key exploratory tool in network analysis and has received much attention in recent years. NMM (Newman’s mixture model) is one of the best models for exploring a range of network structures including community structure, bipartite and core–periphery structures, etc. However, NMM needs to know the number of communities in advance. Therefore, in this study, we have proposed an entropy regularized mixture model (called EMM), which is capable of inferring the number of communities and identifying network structure contained in a network, simultaneously. In the model, by minimizing the entropy of mixing coefficients of NMM using EM (expectation–maximization) solution, the small clusters contained little information can be discarded step by step. The empirical study on both synthetic networks and real networks has shown that the proposed model EMM is superior to the state-of-the-art methods.

Suggested Citation

  • Chang, Zhenhai & Yin, Xianjun & Jia, Caiyan & Wang, Xiaoyang, 2018. "Mixture models with entropy regularization for community detection in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 339-350.
    • Handle: RePEc:eee:phsmap:v:496:y:2018:i:c:p:339-350
    DOI: 10.1016/j.physa.2018.01.002
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    References listed on IDEAS

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    Cited by:

    1. Maihami, Vafa & Yaghmaee, Farzin, 2018. "Automatic image annotation using community detection in neighbor images," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 123-132.

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