Unsupervised community detection in attributed networks based on mutual information maximization

Junyou Zhu, Xianghua Li, Chao Gao, Zhen Wang, Jurgen Kurths

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Community detection is of great significance for understanding network functions and behaviors. With the successful application of deep learning in network-based analyses, recent studies have turned to utilizing graph convolutional networks (GCNs) to this problem due to their capability in capturing network attributes. Nevertheless, most existing GCN-based community detection approaches are semi-supervised and local structure-aware, even though community detection is an unsupervised learning problem essentially. In this paper, we develop a novel GCN method for unsupervised community detection under the framework of mutual information (MI) maximization, called UCDMI. Specifically, a novel MI maximization mechanism is developed to capture more fine-grained information of the global network structure in an unsupervised manner.Moreover, a new aggregation function is proposed for GCN to distinguish the importance between different neighboring nodes, which enables our method to identify more high-quality node representations and improve the community detection performance. Our extensive experiments demonstrate the effectiveness of our proposed UCDMI compared with several state-of-the-art community detection methods.

Original languageEnglish
Article number113016
JournalNew Journal of Physics
Volume23
Issue number11
DOIs
StatePublished - Nov 2021

Keywords

  • Attributed networks
  • Community detection
  • Graph convolutional networks
  • Mutual information

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