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.
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