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Graph regularized nonnegative matrix tri-factorization for overlapping community detection

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  • Jin, Hong
  • Yu, Wei
  • Li, ShiJun

Abstract

Non-negative Matrix Factorization technique has attracted many interests in overlapping community detection due to its performance and interpretability. However, when adapted to discover community structure the intrinsic geometric information of the network graph is seldom considered. In view of this, we proposed a novel NMF based algorithm called Graph regularized nonnegative matrix tri-factorization (GNMTF) model, which incorporates the intrinsic geometrical properties of the network graph by manifold regularization. Moreover, by using three factor matrices we can not only explicitly obtain the community membership of each node but also learn the interaction among different communities. The experimental results on two well-known real world networks and a benchmark network demonstrate the effectiveness of the algorithm over the representative non-negative matrix factorization based method.

Suggested Citation

  • Jin, Hong & Yu, Wei & Li, ShiJun, 2019. "Graph regularized nonnegative matrix tri-factorization for overlapping community detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 376-387.
    • Handle: RePEc:eee:phsmap:v:515:y:2019:i:c:p:376-387
    DOI: 10.1016/j.physa.2018.09.093
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    References listed on IDEAS

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    1. Zhong-Yuan Zhang & Yong-Yeol Ahn, 2015. "Community detection in bipartite networks using weighted symmetric binary matrix factorization," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 26(09), pages 1-14.
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