Journal
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
Volume 496, Issue -, Pages 339-350Publisher
ELSEVIER
DOI: 10.1016/j.physa.2018.01.002
Keywords
Complex networks; Community detection; Mixture models; Entropy
Categories
Funding
- National Science Foundation of China [61473030]
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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. (C) 2018 Elsevier B.V. All rights reserved.
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