TY - JOUR
T1 - Unsupervised community detection in attributed networks based on mutual information maximization
AU - Zhu, Junyou
AU - Li, Xianghua
AU - Gao, Chao
AU - Wang, Zhen
AU - Kurths, Jurgen
N1 - Publisher Copyright:
© 2021 Institute of Physics Publishing. All rights reserved.
PY - 2021/11
Y1 - 2021/11
N2 - 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.
AB - 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.
KW - Attributed networks
KW - Community detection
KW - Graph convolutional networks
KW - Mutual information
UR - http://www.scopus.com/inward/record.url?scp=85119655466&partnerID=8YFLogxK
U2 - 10.1088/1367-2630/ac2fbd
DO - 10.1088/1367-2630/ac2fbd
M3 - 文章
AN - SCOPUS:85119655466
SN - 1367-2630
VL - 23
JO - New Journal of Physics
JF - New Journal of Physics
IS - 11
M1 - 113016
ER -