An Evolutionary Approach Based on Symmetric Nonnegative Matrix Factorization for Community Detection in Dynamic Networks

Yu PAN  Guyu HU  Zhisong PAN  Shuaihui WANG  Dongsheng SHAO  

IEICE TRANSACTIONS on Information and Systems   Vol.E102-D   No.12   pp.2619-2623
Publication Date: 2019/12/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2019EDL8046
Type of Manuscript: LETTER
Category: Artificial Intelligence, Data Mining
dynamic networks,  community detection,  symmetric nonnegative matrix factorization,  

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Detecting community structures and analyzing temporal evolution in dynamic networks are challenging tasks to explore the inherent characteristics of the complex networks. In this paper, we propose a semi-supervised evolutionary clustering model based on symmetric nonnegative matrix factorization to detect communities in dynamic networks, named sEC-SNMF. We use the results of community partition at the previous time step as the priori information to modify the current network topology, then smooth-out the evolution of the communities and reduce the impact of noise. Furthermore, we introduce a community transition probability matrix to track and analyze the temporal evolutions. Different from previous algorithms, our approach does not need to know the number of communities in advance and can deal with the situation in which the number of communities and nodes varies over time. Extensive experiments on synthetic datasets demonstrate that the proposed method is competitive and has a superior performance.