Deep embedded clustering with distribution consistency preservation for attributed networks

Abstract

Many complex systems in the real world can be characterized as attributed networks. To mine the potential information in these networks, deep embedded clustering, which obtains node representations and clusters simultaneously, has been given much attention in recent years. Under the assumption of consistency for data in different views, the cluster structure of network topology and that of node attributes should be consistent for an attributed network. However, many existing methods ignore this property, even though they separately encode node representations from network topology and node attributes and cluster nodes on representation vectors learned from one of the views. Therefore, in this study, we propose an end-to-end deep embedded clustering model for attributed networks. It utilizes graph autoencoder and node attribute autoencoder to learn node representations and cluster assignments. In addition, a distribution consistency constraint is introduced to maintain the latent consistency of cluster distributions in two views. Extensive experiments on several datasets demonstrate that the proposed model achieves significantly better or competitive performance compared with the state-of-the-art methods. The source code can be found at https://github.com/Zhengymm/DCP.