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This paper introduces a new concept of least community that is as homogeneousas a random graph, and develops a new community detection algorithm from theperspective of homogeneity or heterogeneity. Based on this concept, we adopthead/tail breaks - a newly developed classification scheme for data with aheavy-tailed distribution - and rely on edge betweenness given its heavy-taileddistribution to iteratively partition a network into many heterogeneous andhomogeneous communities. Surprisingly, the derived communities for anyself-organized and/or self-evolved large networks demonstrate very strikingpower laws, implying that there are far more small communities than large ones.This notion of far more small things than large ones constitutes a newfundamental way of thinking for community detection. Keywords: head/tailbreaks, ht-index, scaling, k-means, natural breaks, and classification

Defining Least Community as a Homogeneous Group in Complex Networks