Defining Least Community as a Homogeneous Group in Complex Networks
arXiv:1502.00284 · doi:10.1016/j.physa.2015.02.029
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, and classification
arXiv comment9 pages, 3 figures, 3 tables; Physica A, 2015, xx(x), xx-xx
References in corpus (11)
- Emergence of scaling in random networks
- Community structure in social and biological networks
- Community detection in graphs
- Power-law distributions in empirical data
- Cooperative Game Theory Approaches for Network Partitioning
- Head/tail Breaks: A New Classification Scheme for Data with a Heavy-tailed Distribution
- Ht-Index for Quantifying the Fractal or Scaling Structure of Geographic Features
- The Evolution of Natural Cities from the Perspective of Location-Based Social Media
- Head/tail Breaks for Visualization of City Structure and Dynamics
- Structural constraints in complex networks
- Topological Structure of Urban Street Networks from the Perspective of Degree Correlations