Attributed network community detection based on network embedding and parameter-free clustering

In recent years, many attributednetwork have emerged, such as Facebook networks in social networks, protein networks and academic citation networks. In order to find communities where the nodes are tightly connected and have attributes similar to each other by unsupervised learning and improve the accuracy of community detection to make better analysis of the attributed networks, we propose a two-stage attributed network community detection combined with network embedding and parameter-free clustering. In the first stage, we build an attributed network embedding framework that integrates common neighbor information and node attributes. We define node similarity in terms of local link information, jointly model it with attribute proximity, and then adopt the distributed algorithm to obtain the embedding vector of each node. In the second stage, the number of communities can be decided automatically based on curvature and modularity, and the community detection results can be obtained by clustering the embeddings. The performance experiments of our method compared with some representative approaches are tested on real network datasets. The experimental results validate the effectiveness and superiority of our approach.