Graph Clustering via Variational Graph Embedding

Abstract Graph clustering based on embedding aims to divide nodes with higher similarity into several mutually disjoint groups, but it is not a trivial task to maximumly embed the graph structure and node attributes into the low dimensional feature space. Furthermore, most of the current advanced methods of graph nodes clustering adopt the strategy of separating graph embedding technology and clustering algorithm, and ignore the potential relationship between them. Therefore, we propose an innovative end-to-end graph clustering framework with joint strategy to handle the complex problem in a non-Euclidean space. In terms of learning the graph embedding, we propose a new variational graph auto-encoder algorithm based on the Graph Convolution Network (GCN), which takes into account the boosting influence of joint generative model of graph structure and node attributes on the embedding output. On the basis of embedding representation, we implement a self-training mechanism through the construction of auxiliary distribution to further enhance the prediction of node categories, thereby realizing the unsupervised clustering mode. In addition, the loss contribution of each cluster is normalized to prevent large clusters from distorting the embedding space. Extensive experiments on real-world graph datasets validate our design and demonstrate that our algorithm has highly competitive in graph clustering over state-of-the-art methods.