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Adjusting from disjoint to overlapping community detection of complex networks

Author

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  • Wang, Xiaohua
  • Jiao, Licheng
  • Wu, Jianshe

Abstract

The investigation of community structures is one of the most important problems in the field of complex networks and has countless applications in different disciplines: biology, computer, social sciences, etc. Many community detection algorithms have been developed in various fields recently. The vast majority of these algorithms only find disjoint communities; however, in many real-world networks communities often overlap to some extent. In this paper, we propose an efficient method for adjusting these classical algorithms to match the requirement for discovering overlapping communities in complex networks, which is based on a local definition of community strength. The method can in principle be applied with any clustering algorithm. Tests on a set of computer generated and real-world networks give excellent results. In particular, we show that the method can also allow one to availably analyze the problem of unstable nodes in community detection, which is very helpful for understanding the structural properties of the networks correctly and comprehensively.

Suggested Citation

  • Wang, Xiaohua & Jiao, Licheng & Wu, Jianshe, 2009. "Adjusting from disjoint to overlapping community detection of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(24), pages 5045-5056.
    • Handle: RePEc:eee:phsmap:v:388:y:2009:i:24:p:5045-5056
    DOI: 10.1016/j.physa.2009.08.032
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    Citations

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    Cited by:

    1. Shang, Ronghua & Bai, Jing & Jiao, Licheng & Jin, Chao, 2013. "Community detection based on modularity and an improved genetic algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(5), pages 1215-1231.
    2. Mu, Caihong & Liu, Yong & Liu, Yi & Wu, Jianshe & Jiao, Licheng, 2014. "Two-stage algorithm using influence coefficient for detecting the hierarchical, non-overlapping and overlapping community structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 408(C), pages 47-61.

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