Community Detection Fusing Graph Attention Network

Ruiqiang Guo, Juan Zou, Qianqian Bai, Wei Wang () and Xiaomeng Chang
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Ruiqiang Guo: College of Computer and Cyber Security, Hebei Normal University, Shijiazhuang 050024, China
Juan Zou: Key Laboratory of Intelligent Computing and Information Processing, Ministry of Education, School of Computer Science and School of Cyberspace Science, Xiangtan University, Xiangtan 411105, China
Qianqian Bai: College of Computer and Cyber Security, Hebei Normal University, Shijiazhuang 050024, China
Wei Wang: College of Computer and Cyber Security, Hebei Normal University, Shijiazhuang 050024, China
Xiaomeng Chang: College of Computer and Cyber Security, Hebei Normal University, Shijiazhuang 050024, China

Mathematics, 2022, vol. 10, issue 21, 1-14

Abstract: It has become a tendency to use a combination of autoencoders and graph neural networks for attribute graph clustering to solve the community detection problem. However, the existing methods do not consider the influence differences between node neighborhood information and high-order neighborhood information, and the fusion of structural and attribute features is insufficient. In order to make better use of structural information and attribute information, we propose a model named community detection fusing graph attention network (CDFG). Specifically, we firstly use an autoencoder to learn attribute features. Then the graph attention network not only calculates the influence weight of the neighborhood node on the target node but also adds the high-order neighborhood information to learn the structural features. After that, the two features are initially fused by the balance parameter. The feature fusion module extracts the hidden layer representation of the graph attention layer to calculate the self-correlation matrix, which is multiplied by the node representation obtained by the preliminary fusion to achieve secondary fusion. Finally, the self-supervision mechanism makes it face the community detection task. Experiments are conducted on six real datasets. Using four evaluation metrics, the CDFG model performs better on most datasets, especially for the networks with longer average paths and diameters and smaller clustering coefficients.

Keywords: graph attention network; high-order neighborhood; attribute network; community detection (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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