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Defining least community as a homogeneous group in complex networks

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  • Jiang, Bin
  • Ma, Ding

Abstract

This paper introduces a new concept of least community that is as homogeneous as a random graph, and develops a new community detection algorithm from the perspective of homogeneity or heterogeneity. Based on this concept, we adopt head/tail breaks–a newly developed classification scheme for data with a heavy-tailed distribution–and rely on edge betweenness given its heavy-tailed distribution to iteratively partition a network into many heterogeneous and homogeneous communities. Surprisingly, the derived communities for any self-organized and/or self-evolved large networks demonstrate very striking power laws, implying that there are far more small communities than large ones. This notion of far more small things than large ones constitutes a new fundamental way of thinking for community detection.

Suggested Citation

  • Jiang, Bin & Ma, Ding, 2015. "Defining least community as a homogeneous group in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 154-160.
    • Handle: RePEc:eee:phsmap:v:428:y:2015:i:c:p:154-160
    DOI: 10.1016/j.physa.2015.02.029
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    References listed on IDEAS

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    1. repec:cup:apsrev:v:21:y:1927:i:03:p:619-627_02 is not listed on IDEAS
    2. Bin Jiang & Junjun Yin, 2014. "Ht-Index for Quantifying the Fractal or Scaling Structure of Geographic Features," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 104(3), pages 530-540, May.
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    Cited by:

    1. Jiang, Bin, 2016. "A complex-network perspective on Alexander’s wholeness," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 463(C), pages 475-484.

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