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Differentiated graph regularized non-negative matrix factorization for semi-supervised community detection

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  • Chen, Chunchun
  • Zhu, Wenjie
  • Peng, Bo

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.

Suggested Citation

  • Chen, Chunchun & Zhu, Wenjie & Peng, Bo, 2022. "Differentiated graph regularized non-negative matrix factorization for semi-supervised community detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    • Handle: RePEc:eee:phsmap:v:604:y:2022:i:c:s0378437122004605
    DOI: 10.1016/j.physa.2022.127692
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    References listed on IDEAS

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