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Community detection in bipartite networks using weighted symmetric binary matrix factorization

Author

Listed:
  • Zhong-Yuan Zhang

    (School of Statistics and Mathematics, Central University of Finance and Economics, Beijing, P. R. China)

  • Yong-Yeol Ahn

    (School of Informatics and Computing, Indiana University Bloomington, IN, USA)

Abstract

In this paper, we propose weighted symmetric binary matrix factorization (wSBMF) framework to detect overlapping communities in bipartite networks, which describes the relationships between two types of nodes. Our method improves performance by recognizing the distinction between two types of missing edges — ones among the nodes in each node type and the others between two node types. Our method can also explicitly assign community membership and distinguish outliers from overlapping nodes, as well as incorporating existing knowledge on the network. We propose a generalized partition density for bipartite networks as a quality function, which identifies the most appropriate number of communities. The experimental results on both synthetic and real-world networks demonstrate the effectiveness of our method.

Suggested Citation

  • 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.
    • Handle: RePEc:wsi:ijmpcx:v:26:y:2015:i:09:n:s0129183115500965
    DOI: 10.1142/S0129183115500965
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    Citations

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    Cited by:

    1. You, Tao & Cheng, Hui-Min & Ning, Yi-Zi & Shia, Ben-Chang & Zhang, Zhong-Yuan, 2016. "Community detection in complex networks using density-based clustering algorithm and manifold learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 464(C), pages 221-230.
    2. 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.
    3. Zhang, Zhong-Yuan & Gai, Yujie & Wang, Yu-Fei & Cheng, Hui-Min & Liu, Xin, 2018. "On equivalence of likelihood maximization of stochastic block model and constrained nonnegative matrix factorization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 687-697.
    4. Yubo Peng & Bofeng Zhang & Furong Chang, 2021. "Overlapping Community Detection of Bipartite Networks Based on a Novel Community Density," Future Internet, MDPI, vol. 13(4), pages 1-21, March.

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