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Constrained Symmetric Non-Negative Matrix Factorization with Deep Autoencoders for Community Detection

Author

Listed:
  • Wei Zhang

    (College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China)

  • Shanshan Yu

    (Training and Basic Education Management Office, Southwest University, Chongqing 400715, China
    Key Laboratory of Cyber-Physical Fusion Intelligent Computing (South-Central Minzu University), State Ethnic Affairs Commission, Wuhan 430074, China)

  • Ling Wang

    (College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China)

  • Wei Guo

    (College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China)

  • Man-Fai Leung

    (School of Computing and Information Science, Faculty of Science and Engineering, Anglia Ruskin University, Cambridge CB1 1PT, UK)

Abstract

Recently, community detection has emerged as a prominent research area in the analysis of complex network structures. Community detection models based on non-negative matrix factorization (NMF) are shallow and fail to fully discover the internal structure of complex networks. Thus, this article introduces a novel constrained symmetric non-negative matrix factorization with deep autoencoders (CSDNMF) as a solution to this issue. The model possesses the following advantages: (1) By integrating a deep autoencoder to discern the latent attributes bridging the original network and community assignments, it adeptly captures hierarchical information. (2) Introducing a graph regularizer facilitates a thorough comprehension of the community structure inherent within the target network. (3) By integrating a symmetry regularizer, the model’s capacity to learn undirected networks is augmented, thereby facilitating the precise detection of symmetry within the target network. The proposed CSDNMF model exhibits superior performance in community detection when compared to state-of-the-art models, as demonstrated by eight experimental results conducted on real-world networks.

Suggested Citation

  • Wei Zhang & Shanshan Yu & Ling Wang & Wei Guo & Man-Fai Leung, 2024. "Constrained Symmetric Non-Negative Matrix Factorization with Deep Autoencoders for Community Detection," Mathematics, MDPI, vol. 12(10), pages 1-17, May.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:10:p:1554-:d:1395908
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    References listed on IDEAS

    as
    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).
    2. Jun Hu & Hongxu Zhang & Hongjian Liu & Xiaoyang Yu, 2021. "A survey on sliding mode control for networked control systems," International Journal of Systems Science, Taylor & Francis Journals, vol. 52(6), pages 1129-1147, April.
    3. S. Taheri & G. Hesamian, 2013. "A generalization of the Wilcoxon signed-rank test and its applications," Statistical Papers, Springer, vol. 54(2), pages 457-470, May.
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