Variational Graph Autoencoders Method Based on Attentional Mechanisms for Overlapping Community Detection

Abstract

AbstractDetecting overlapping communities of an attribute network is an important and difficult issue in network science. Traditional methods for overlapping community detection generally considered only its topological structure and neglected node attribute so that the detected communities could be inaccurate. Graph neural networks (GNN) have recently gained attention because of their learning representation ability for node attributes and the topology of a graph and demonstrating superior performance in natural language processing, data mining and pattern recognition, etc. However, GNN methods are still less for overlapping community detection. In this paper, we present an overlapping community detection method based on GNN for attribute networks. It applies a variational graph autoencoder based on attentional mechanisms to learn the representation of nodes in the graph and enhances the representation learning capability of GNN through semi-supervised learning. Various real-world networks of publicly available datasets are used to verify the effectiveness of the proposed approach. The experimental results show that the proposed method outperforms many other methods.KeywordsOverlapping community detectionAttentional mechanismsVariational graph autoencoder