Community Detection in Feature-Rich Networks Using Data Recovery Approach

Journal of Classification. 2022. Vol. 39. P. 432–462.

Mirkin B., Shalileh S.

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: data recovery approach, community detection algorithms, clustering algorithms, Community Detection, Feature-Rich Network, Least squares data recovery

Publication based on the results of: Theoretical and Applied Problems of Data Analysis and Decision Making in the Socio-Economic and Financial Spheres (2022)