Detection of an unspecified number of communities in feature-rich networks

P. 1–12.

Shalileh S., Mirkin B.

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. Existing approaches require the number of communities pre-specified. We apply the so-called data recovery approach to allow a relaxation of the criterion for finding communities 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. © 2020 CEUR-WS. All rights reserved.

Keywords: network analysis, clustering, Community detection, Feature-rich Networks

In book

Proceedings of MARAMI 2020 - Modèles & Analyse des Réseaux : Approches Mathématiques & Informatiques - The 11th Conference on Network Modeling and Analysis(Vol-2750)

Vol. Vol-2750: Modèles & Analyse des Réseaux : Approches Mathématiques & Informatiques - Network Modeling and Analysis 2020. , CEUR-WS.org, 2020.