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- W3123160711 abstract "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." @default.
- W3123160711 created "2021-02-01" @default.
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- W3123160711 creator A5088955392 @default.
- W3123160711 date "2022-03-01" @default.
- W3123160711 modified "2023-10-18" @default.
- W3123160711 title "Symmetric Nonnegative Matrix Factorization-Based Community Detection Models and Their Convergence Analysis" @default.
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- W3123160711 doi "https://doi.org/10.1109/tnnls.2020.3041360" @default.
- W3123160711 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33513110" @default.
- W3123160711 hasPublicationYear "2022" @default.
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