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Symmetric Nonnegative Matrix Factorization-Based Community Detection Models and Their Convergence Analysis
Luo, Xin1,2,3; Liu, Zhigang1,2,4; Jin, Long1,2,3; Zhou, Yue1,2,4; Zhou, MengChu5,6
2021-01-29
摘要Community detection is a popular yet thorny issue in social network analysis. A symmetric and nonnegative matrix factorization (SNMF) model based on a nonnegative multiplicative update (NMU) scheme is frequently adopted to address it. Current research mainly focuses on integrating additional information into it without considering the effects of a learning scheme. This study aims to implement highly accurate community detectors via the connections between an SNMF-based community detector's detection accuracy and an NMU scheme's scaling factor. The main idea is to adjust such scaling factor via a linear or nonlinear strategy, thereby innovatively implementing several scaling-factor-adjusted NMU schemes. They are applied to SNMF and graph-regularized SNMF models to achieve four novel SNMF-based community detectors. Theoretical studies indicate that with the proposed schemes and proper hyperparameter settings, each model can: 1) keep its loss function nonincreasing during its training process and 2) converge to a stationary point. Empirical studies on eight social networks show that they achieve significant accuracy gain in community detection over the state-of-the-art community detectors.
关键词Detectors Convergence Symmetric matrices Social networking (online) Analytical models Tuning Computational modeling Community detection convergence analysis graph regularization nonnegative multiplicative update (NMU) social network analysis symmetric and nonnegative matrix factorization (SNMF)
DOI10.1109/TNNLS.2020.3041360
发表期刊IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN2162-237X
页码13
通讯作者Luo, Xin(luoxin21@cigit.ac.cn) ; Zhou, MengChu(zhou@njit.edu)
收录类别SCI
WOS记录号WOS:000732334900001
语种英语