On the relationship between Gaussian stochastic blockmodels and label propagation algorithms. (11th March 2015)

• Record Type: Journal Article
• Authors: Zhang, Junhao; Chen, Tongfei; Hu, Junfeng
• Is Part Of: Journal of statistical mechanics. (2015:Mar.)
• Publication Date: 2015-03-11
• Subjects: 11; 11/010 -- 11/080 -- 11/011; Statistical mechanics -- Periodicals; Mechanics -- Statistical methods -- Periodicals; 530.1305
• DOI: 10.1088/1742-5468/2015/03/P03009
• Languages: English
• ISSNs: 1742-5468

Abstract

Abstract: The problem of community detection has received great attention in recent years. Many methods have been proposed to discover communities in networks. In this paper, we propose a Gaussian stochastic blockmodel that uses Gaussian distributions to fit weight of edges in networks for non-overlapping community detection. The maximum likelihood estimation of this model has the same objective function as general label propagation with node preference. The node preference of a specific vertex turns out to be a value proportional to the intra-community eigenvector centrality (the corresponding entry in principal eigenvector of the adjacency matrix of the subgraph inside that vertex's community) under maximum likelihood estimation. Additionally, the maximum likelihood estimation of a constrained version of our model is highly related to another extension of the label propagation algorithm, namely, the label propagation algorithm under constraint. Experiments show that the proposed Gaussian stochastic blockmodel performs well on various benchmark networks.