Unsupervised Graph Neural Network with Self-Expressive Attention for Community Detection

Abstract

Community detection is an important task in graph analysis, and it is of great significance in reality. Recently, unsupervised learning has been widely used in community detection tasks. However, only a few community detection models combine unsupervised learning with graph neural networks (GNNs). To this end, in this paper, we combine GNNs with unsupervised learning to propose a new model, Unsupervised graph neural network with Self-expressive attention for Community detection (USCom). We first use the graph attention encoder to generate node embeddings. Then we apply the self-expressive principle to optimize the node embeddings to make them more suitable for community detection tasks. Finally, we utilize a four-layer perceptron for community detection. The experimental results show that the model proposed in this paper outperforms the comparison baselines on community detection tasks.