In recent years, attributed networks are increasingly available for us. How to leverage attri-bute information to gain a better performance of community detection has attracted grow-ing attention. Most established approaches focus on exploiting attribute information based on the observed homophily assumption that nodes with similar attributes are more likely to be connected. Since not all attributes are truly relevant for the formation of communi-ties, such an assumption may not hold in real-world attributed networks. In many cases, only a subset of attributes are the key factors to make nodes connected to form communi-ties and they may differ largely for different communities. In this paper, we propose a new community detection approach in attributed networks, called SOA, by examining the Subspaces Of Attributes. The basic idea is to regard the community detection as an opti-mization problem, which aims at learning a new similarity matrix to maximize the homo-phily. By employing an efficient optimization strategy, the high-quality communities as well as their corresponding relevant attributes (i.e., attribute subspaces) are automatically identified. Extensive experimental results on both synthetic and real-world networks have demonstrated the effectiveness of SOA, and shown its superiority over state-of-the-art approaches.(c) 2022 Published by Elsevier Inc.
Community detectionSubspace clusteringAttributed networkSpectral clusteringHomophily theoryShao)
NSTL
Elsevier
