Community detection based on unsupervised attributed network embedding. (1st March 2023) - Zhou, Xinchuang; Su, Lingtao; Li, Xiangju; Zhao, Zhongying; Li, Chao
Abstract: Community detection methods based on attribute network representation learning are receiving increasing attention. However, few existing works are focused exclusively on unsupervised network representation learning for the task of community detection. They mainly capture information about the topology or attributes of the network, but do not fully utilize clustering-oriented information. In this paper, we present a community detection algorithm based on unsupervised attributed network embedding (CDBNE) to resolve the above issues. To be specific, we propose a framework that learns the representation based on network structure and attribute information and the clustering-oriented representation simultaneously. The framework includes the graph attention auto-encoder module, the modularity maximization module, and the self-training clustering module. Firstly, CDBNE encodes the topology structure and the node attribute with the graph attention mechanism. Secondly, it captures the mesoscopic community structure with modularity maximization. Finally, the self-training clustering module optimizes the representation learning process in a self-supervised manner to obtain high-quality node representation. The performance of CDBNE is verified with experiments on community detection tasks. According to the results on three datasets, CDBNE outperforms the state-of-the-art methods. The implementation of CDBNE is available at https://github.com/xidizxc/CDBNE . Highlights: We present a Community Detection algorithm based on attributed Network Embedding. It jointly model topology and attributes with the graph attention auto-encoder. We conduct experiments and compare the performance with 10 competitive baselines. The experiment results demonstrate the effectiveness of the proposed community detection model.