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Modularized tri-factor nonnegative matrix factorization for community detection enhancement

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  • Yan, Chao
  • Chang, Zhenhai

Abstract

Community structure detection is a fundamental problem for understanding the relationship between the topology structures and the functions of complex networks. NMF-based models are a promising method for identifying communities from networks, but most of them require the number of communities in advance, which is inconvenient for real applications. Also, the basic NMF model could not reflect the characteristics of networks more comprehensively under the sole nonnegative constraint. In this paper, we develop a novel modularized tri-factor nonnegative matrix factorization model which combines the modularized information as the regularization term, leading to improved performance in community detection. Besides, we utilize general modularity density to determine the number of communities. Finally, the effectiveness of our approach is demonstrated on both synthetic and real-world networks.

Suggested Citation

  • Yan, Chao & Chang, Zhenhai, 2019. "Modularized tri-factor nonnegative matrix factorization for community detection enhancement," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 533(C).
    • Handle: RePEc:eee:phsmap:v:533:y:2019:i:c:s0378437119311914
    DOI: 10.1016/j.physa.2019.122050
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    Cited by:

    1. Yan, Chao & Chang, Zhenhai, 2020. "Modularized convex nonnegative matrix factorization for community detection in signed and unsigned networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).

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