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- W4283021971 abstract "Semi-supervised non-negative matrix factorization (Semi-NMF) has been widely used in community detection by employing the side information. However, the graph used in previous Semi-NMF methods only takes into account single graph construction, being aware of specific similarity measurements among the community nodes. In this paper, we propose a novel approach, named Differentiated Graph regularized Non-negative Matrix Factorization (DGNMF), for semi-supervised community detection by leveraging the paired constraints between network nodes. In particular, the similarity and dissimilarity constraints on must-link and cannot-link data samples are imposed respectively to construct a differentiated graph that is involved into the proposed algorithm in a form of dual-sparse NMF problem. To solve the optimization problem, we propose an alternate iterative algorithm and demonstrate its convergence theoretically. Extensive experiments on two artificial networks and ten real-world networks show that the proposed DGNMF can effectively improve the accuracy of community detection compared with the state-of-the-art NMF-based approaches." @default.
- W4283021971 created "2022-06-18" @default.
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- W4283021971 date "2022-10-01" @default.
- W4283021971 modified "2023-10-16" @default.
- W4283021971 title "Differentiated graph regularized non-negative matrix factorization for semi-supervised community detection" @default.
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- W4283021971 doi "https://doi.org/10.1016/j.physa.2022.127692" @default.
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