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Community detection algorithm based on nonnegative matrix factorization and pairwise constraints

Author

Listed:
  • Lu, Hong
  • Sang, Xiaoshuang
  • Zhao, Qinghua
  • Lu, Jianfeng

Abstract

Community detection is a critical issue in the field of complex networks. Nonnegative matrix factorization (NMF) has been one of the hot research topics in community detection. In the real world, only topology information is inadequate to detect community structure accurately. To utilize the limited supervised information effectively, we propose two algorithms for community detection, which combine NMF or (Symmetric NMF) with pairwise constraints to deal with linearly separable data and nonlinearly separable data, respectively. Currently, most of NMF-based community detection methods need the number of communities in advance or searching among all candidates with different K. To address this issue, we use singular value decomposition to reveal the number of communities automatically. Experimental results demonstrate that the proposed methods can produce higher clustering performance compared with some state-of-the-art methods.

Suggested Citation

  • Lu, Hong & Sang, Xiaoshuang & Zhao, Qinghua & Lu, Jianfeng, 2020. "Community detection algorithm based on nonnegative matrix factorization and pairwise constraints," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    • Handle: RePEc:eee:phsmap:v:545:y:2020:i:c:s037843711931948x
    DOI: 10.1016/j.physa.2019.123491
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    References listed on IDEAS

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    Cited by:

    1. Fengqin Tang & Xuejing Zhao & Cuixia Li, 2023. "Community Detection in Multilayer Networks Based on Matrix Factorization and Spectral Embedding Method," Mathematics, MDPI, vol. 11(7), pages 1-19, March.
    2. 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).

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