Community detection with structural and attribute similarities

ABSTRACT

An important problem in network analysis is to identify significant communities. Most of the real-world data sets exhibit a certain topological structure between nodes and the attributes describing them. In this paper, we propose a new community detection criterion considering both structural similarities and attribute similarities. The clustering method integrates the cost of clustering node attributes with the cost of clustering the structural information via the normalized modularity. We show that the joint clustering problem can be formulated as a spectral relaxation problem. The proposed algorithm is capable of learning the degree of contributions of individual node attributes. A number of numerical studies involving simulated and real data sets demonstrate the effectiveness of the proposed method.

KEYWORDS:

• Community detection
• spectral clustering
• modularity
• stochastic block model

2010 MATHEMATICS SUBJECT CLASSIFICATIONS:

• 62-07
• 62P25

Acknowledgements

The authors would like to thank an associate editor and anonymous referees for their careful reading and helpful comments. The authors also express sincere thankfulness for Professor Bingyi Jing (Hong Kong University of Science and Technology) for conducting experimental tests of our algorithm and for discussions related to this paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Fengqin Tang http://orcid.org/0000-0001-7234-1419

Wenwen Ding http://orcid.org/0000-0001-8582-2078

Additional information

Funding

This work is supported by the National Natural Science Foundation of China (11301236), The Key Project of Natural Science Foundation of the Education Department of Anhui Province (KJ2017A377, KJ2017A376), Anhui Provincial Natural Science Foundation of China (1608085QG169).