A Data Recovery Method for Community Detection in Feature-Rich Networks

Ch. 05. P. 99–105.

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. 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, so that the process can be interpreted as that of detecting communities indeed. 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: cluster analysis, community detection, Feature-rich Networks, Attributed Network, Data Recovery, One by One Clustering

In book

2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

Association for Computing Machinery (ACM), 2020.