Correlation enhanced modularity-based belief propagation method for community detection in networks. (11th May 2016)

• Record Type: Journal Article
• Authors: Lai, Darong; Shu, Xin; Nardini, Christine
• Is Part Of: Journal of statistical mechanics. (2016:May)
• Publication Date: 2016-05-11
• Subjects: Statistical mechanics -- Periodicals; Mechanics -- Statistical methods -- Periodicals; 530.1305
• DOI: 10.1088/1742-5468/2016/05/053301
• Languages: English
• ISSNs: 1742-5468

Abstract

Abstract: Community structure is an important feature of networks, and the correct detection of communities is a fundamental problem in network analysis. Statistical inference has recently been proposed for successful detection, provided the number of communities can be appropriately estimated a priori . In the absence of such information, model selection by determination of the number of communities remains an issue. We show here that correlation between communities from a highly parceled partition can be used to estimate a narrow range of variation for the real number of communities. This range, further elaborated by modularity-based belief propagation, correctly identifies communities. Testing on synthetic networks generated by a stochastic block model and a set of real-world networks shows that our method can alleviate the effects of modularity fluctuations well and enhance the ability of community detection of the bare modularity-based belief propagation method.