A General Probabilistic Framework for Detecting Community Structure in Networks
Based on Newman's fast algorithm, in this paper the authors develop a general probabilistic framework for detecting community structure in a network. The key idea of their generalization is to characterize a network (graph) by a bivariate distribution that specifies the probability of the two vertices appearing at both ends of a randomly selected path in the graph. With such a bivariate distribution, they give a probabilistic definition of a community and a definition of a modularity index. To detect communities in a network, they propose a class of distribution-based clustering algorithms that have comparable computational complexity to that of Newman's fast algorithm.