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Mixture models with entropy regularization for community detection in networks

Zhenhai Chang, Xianjun Yin, Caiyan Jia and Xiaoyang Wang

Physica A: Statistical Mechanics and its Applications, 2018, vol. 496, issue C, 339-350

Abstract: Community detection is a key exploratory tool in network analysis and has received much attention in recent years. NMM (Newman’s mixture model) is one of the best models for exploring a range of network structures including community structure, bipartite and core–periphery structures, etc. However, NMM needs to know the number of communities in advance. Therefore, in this study, we have proposed an entropy regularized mixture model (called EMM), which is capable of inferring the number of communities and identifying network structure contained in a network, simultaneously. In the model, by minimizing the entropy of mixing coefficients of NMM using EM (expectation–maximization) solution, the small clusters contained little information can be discarded step by step. The empirical study on both synthetic networks and real networks has shown that the proposed model EMM is superior to the state-of-the-art methods.

Keywords: Complex networks; Community detection; Mixture models; Entropy (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:496:y:2018:i:c:p:339-350

DOI: 10.1016/j.physa.2018.01.002

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