Community Detection with Graph Convolutional Auto-Encoder and Deep Clustering

Ru Wang, Peipei Wang, Lin Li, Peiyu Liu

Abstract: Communities usually exhibit similar opinions, similar functions, or similar purposes, and recent years have witnessed the the resurgence of community detection in various fields. The node attribute network has gradually become the mainstream of the community network, however, most of existing community detection methods are limited by the high-dimensional node attribute and network topology, leading to the suboptimal performance. Inspired by recent deep learning-based community detection methods, we focus on building an unsupervised deep learning architecture to handle high-dimensional data in complex networks for community detection. To this end, we propose a novel Community Detection method based Deep Clustering and Graph Convolution auto-encoder Network (CD-DCGCN). Our CD-DCGCN designs an end-to-end framework consisting of dual auto-operations, one is a graph convolution auto-encoder, the other is community auto-detection. In addition, we realize the cooperative work of the dual auto-operations by constructing a joint optimized function. Our experimental results on nine attribute network benchmark datasets show that the proposed CD-DCGCN can obtain promising performance compared with several popular baseline methods.