期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 33, 期 3, 页码 1203-1215出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2020.3041360
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)
类别
资金
- National Natural Science Foundation of China [61772493, 91646114]
- Natural Science Foundation of Chongqing (China) [cstc2019jcyjjqX0013, cstc2020jcyjzdxmX0028]
- Chinese Academy of Sciences (CAS) Light of West China Program
- CAAI-Huawei MindSpore Open Fund [CAAIXSJLJJ-2020-004B]
- Chongqing Research Program of Technology Innovation and Application [cstc2019jscxfxydX0027]
- Deanship of Scientific Research (DSR) at King Abdulaziz University [RG-21-135-39]
- Pioneer Hundred Talents Program of Chinese Academy of Sciences
This study achieves highly accurate community detectors by adjusting the scaling factor in the SNMF model, leading to significant accuracy gains in community detection over the state-of-the-art detectors.
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.
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