Defining least community as a homogeneous group in complex networks

Bin Jiang and Ding Ma

Physica A: Statistical Mechanics and its Applications, 2015, vol. 428, issue C, 154-160

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.

Keywords: Head/tail breaks; ht-index; Scaling; k-means; Natural breaks; Classification (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:428:y:2015:i:c:p:154-160

DOI: 10.1016/j.physa.2015.02.029

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