GRAE: Graph Recurrent Autoencoder for Multi-view Graph Clustering

Multi-view graph clustering aims to discover communities or groups in the graph with multiple views, which usually can supply more comprehensive information than that single-view graph clustering. With the increasing scale of complex data from the real world, multi-view graph clustering has drawn much attention. It has a solid theoretical foundation and high effectiveness in applications such as data mining and social network analysis. However, Most existing methods obtain the clustering result only through the shared feature representations, defectively overlooking the unique features of multiple views. To fill this gap, a Graph Recurrent AutoEncoder (GRAE) is proposed for attributed multi-view graph clustering, which can attain node representation well by learning different view features. Specifically, we first design a global graph autoencoder and a partial graph autoencoder to extract the shared features and the unique features of all views, respectively, which can better represent the nodes in the graph. Then, from the perspective of representation fusion, we adopt an adaptive weight learning method to fuse the different features according to the importance of features. Moreover, we investigate a self-training clustering method to optimize a clustering objective for improving the clustering effect. Finally, we conducted a large number of experiments on three real-world datasets, demonstrating the superior performance of our proposed GRAE model on the multi-view graph clustering task.