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Community Detection in Feature-Rich Networks Using Data Recovery Approach

Author

Listed:
  • Boris Mirkin

    (HSE University
    Birkbeck University of London)

  • Soroosh Shalileh

    (HSE University)

Abstract

The problem of community detection in a network with features at its nodes takes into account both the graph structure and node features. The goal is to find relatively dense groups of interconnected entities sharing some features in common. There have been several approaches proposed for that. We apply the so-called data recovery approach to the problem by combining the least-squares recovery criteria for both the graph structure and node features. In this way, we obtain a new clustering criterion and a corresponding algorithm for finding clusters one-by-one. We show that our proposed method is effective on real-world data, as well as on synthetic data involving either only quantitative features or only categorical attributes or both. In the cases at which attributes are categorical, state-of-the-art algorithms are available. Our algorithm appears competitive against them.

Suggested Citation

  • Boris Mirkin & Soroosh Shalileh, 2022. "Community Detection in Feature-Rich Networks Using Data Recovery Approach," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 432-462, November.
    • Handle: RePEc:spr:jclass:v:39:y:2022:i:3:d:10.1007_s00357-022-09416-w
    DOI: 10.1007/s00357-022-09416-w
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    References listed on IDEAS

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    1. Mark Chiang & Boris Mirkin, 2010. "Intelligent Choice of the Number of Clusters in K-Means Clustering: An Experimental Study with Different Cluster Spreads," Journal of Classification, Springer;The Classification Society, vol. 27(1), pages 3-40, March.
    2. Jin, Hong & Yu, Wei & Li, ShiJun, 2018. "A clustering algorithm for determining community structure in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 980-993.
    3. Depril, Dirk & Van Mechelen, Iven & Mirkin, Boris, 2008. "Algorithms for additive clustering of rectangular data tables," Computational Statistics & Data Analysis, Elsevier, vol. 52(11), pages 4923-4938, July.
    4. B. Mirkin, 1987. "Additive clustering and qualitative factor analysis methods for similarity matrices," Journal of Classification, Springer;The Classification Society, vol. 4(1), pages 7-31, March.
    5. Wang, Dong & Zhao, Yi, 2019. "Network community detection from the perspective of time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 522(C), pages 205-214.
    6. M. E. J. Newman & Aaron Clauset, 2016. "Structure and inference in annotated networks," Nature Communications, Nature, vol. 7(1), pages 1-11, September.
    7. Ekaterina Kovaleva & Boris Mirkin, 2015. "Bisecting K-Means and 1D Projection Divisive Clustering: A Unified Framework and Experimental Comparison," Journal of Classification, Springer;The Classification Society, vol. 32(3), pages 414-442, October.
    8. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    9. Li, Yafang & Jia, Caiyan & Yu, Jian, 2015. "A parameter-free community detection method based on centrality and dispersion of nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 321-334.
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    Cited by:

    1. Soroosh Shalileh, 2023. "An Effective Partitional Crisp Clustering Method Using Gradient Descent Approach," Mathematics, MDPI, vol. 11(12), pages 1-23, June.

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