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Spectral clustering-based community detection using graph distance and node attributes

Author

Listed:
  • Fengqin Tang

    (Huaibei Normal University)

  • Chunning Wang

    (Lanzhou University)

  • Jinxia Su

    (Lanzhou University)

  • Yuanyuan Wang

    (Lanzhou University)

Abstract

Community detection is one of the main research topics in network analysis. Most network data reveal a certain structural relationship between nodes and provide attributes describing them. Utilizing available node attributes can help uncover latent communities from an observed network. In this paper, we propose a method of uncovering latent communities using both network structural information and node attributes so that the nodes within each community not only connect to other nodes in similar patterns but also share homogeneous attributes. The proposed method transforms the graph distance of nodes to structural similarity via the Gaussian kernel function. The attribute similarity between nodes is also measured by the Gaussian kernel function. Our method takes advantage of spectral clustering by appending node attributes to the node representation obtained from the network structure. Further, the proposed method has the ability to automatically learn the degree to which different attributes contribute. The solid performance of the proposed method is demonstrated in simulated data and four real-world networks.

Suggested Citation

  • Fengqin Tang & Chunning Wang & Jinxia Su & Yuanyuan Wang, 2020. "Spectral clustering-based community detection using graph distance and node attributes," Computational Statistics, Springer, vol. 35(1), pages 69-94, March.
    • Handle: RePEc:spr:compst:v:35:y:2020:i:1:d:10.1007_s00180-019-00909-8
    DOI: 10.1007/s00180-019-00909-8
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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.

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