Community Detection in Feature-Rich Networks Using Data Recovery Approach

Boris Mirkin () and Soroosh Shalileh ()
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Boris Mirkin: HSE University
Soroosh Shalileh: HSE University

Journal of Classification, 2022, vol. 39, issue 3, No 3, 432-462

Abstract: 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.

Keywords: Attributed network; Feature-rich network; Cluster analysis; Community detection; Data recovery; One by one clustering (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s00357-022-09416-w

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