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- W4318586060 abstract "Community detection aims at finding all densely connected communities in a network, which serves as a fundamental graph tool for many applications, such as identification of protein functional modules, image segmentation, social circle discovery, to name a few. Recently, nonnegative matrix factorization (NMF)-based community detection methods have attracted significant attention. However, most existing methods neglect the multihop connectivity patterns in a network, which turn out to be practically useful for community detection. In this article, we first propose a novel community detection method, namely multihop NMF (MHNMF for brevity), which takes into account the multihop connectivity patterns in a network. Subsequently, we derive an efficient algorithm to optimize MHNMF and theoretically analyze its computational complexity and convergence. Experimental results on 12 real-world benchmark networks demonstrate that MHNMF outperforms 12 state-of-the-art community detection methods." @default.
- W4318586060 created "2023-01-31" @default.
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- W4318586060 date "2023-01-01" @default.
- W4318586060 modified "2023-10-01" @default.
- W4318586060 title "Community Detection via Multihop Nonnegative Matrix Factorization" @default.
- W4318586060 doi "https://doi.org/10.1109/tnnls.2023.3238419" @default.
- W4318586060 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37022252" @default.
- W4318586060 hasPublicationYear "2023" @default.
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