  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, and classification
