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Highly-Accurate Community Detection via Pointwise Mutual Information-Incorporated Symmetric Non-Negative Matrix Factorization
Luo, Xin1,2,3; Liu, Zhigang1,2,4; Shang, Mingsheng1,2,3; Lou, Jungang5; Zhou, MengChu6
2021
摘要Community detection, aiming at determining correct affiliation of each node in a network, is a critical task of complex network analysis. Owing to its high efficiency, Symmetric and Non-negative Matrix Factorization (SNMF) is frequently adopted to handle this task. However, existing SNMF models mostly focus on a network's first-order topological information described by its adjacency matrix without considering the implicit associations among involved nodes. To address this issue, this study proposes a Pointwise mutual information-incorporated and Graph-regularized SNMF (PGS) model. It uses a) Pointwise Mutual Information to quantify implicit associations among nodes, thereby completing the missing but crucial information among critical nodes in a uniform way; b) graph-regularization to achieve precise representation of local topology, and c) SNMF to implement efficient community detection. Empirical studies on eight real-world social networks generated by industrial applications demonstrate that a PGS model achieves significantly higher accuracy gain in community detection than state-of-the-art community detectors.
关键词Detectors Symmetric matrices Image edge detection Social networking (online) Topology Measurement Knowledge engineering Computational Intelligence Social Network Network Representation Community Detection Pointwise Mutual Information Symmetric and Non-negative Matrix Factorization Graph-regularization
DOI10.1109/TNSE.2020.3040407
发表期刊IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
ISSN2327-4697
卷号8期号:1页码:463-476
通讯作者Lou, Jungang(loujungang0210@hotmail.com) ; Zhou, MengChu(zhou@njit.edu)
收录类别SCI
WOS记录号WOS:000631202700037
语种英语