Variational Co-embedding Learning for Attributed Network Clustering

Abstract--Recent works for attributed network clustering utilize graph convolution to obtain node embeddings and simultaneously perform clustering assignments on the embedding space. It is effective since graph convolution combines the structural and attributive information for node embedding learning. However, a major limitation of such works is that the graph convolution only incorporates the attribute information from the local neighborhood of nodes but fails to exploit the mutual affinities between nodes and attributes. In this regard, we propose a variational co-embedding learning model for attributed network clustering (VCLANC). VCLANC is composed of dual variational auto-encoders to simultaneously embed nodes and attributes. Relying on this, the mutual affinity information between nodes and attributes could be reconstructed from the embedding space and served as extra self-supervised knowledge for representation learning. At the same time, trainable Gaussian mixture model is used as priors to infer the node clustering assignments. T o strengthen the performance of the inferred clusters, we use a mutual distance loss on the centers of the Gaussian priors and a clustering assignment hardening loss on the node embeddings. Experimental results on four real-world attributed network datasets demonstrate the effectiveness of the proposed VCLANC for attributed network clustering. Finding accurate communities or clusters in an attributed network is critical to understand the complex network structures for many downstream applications like group recommendation, user-targeted online advertising, and disease protein discovery [1], [2], [3]. Though the clustering for attributed network brings many important applications, but it also poses significant new challenges. Firstly, as the attributed network includes not only structural connections but also attribute values, it is difficult to naturally combine and leverage the two types of information in the process of clustering [4]. Furthermore, it is usually hard to find supervision information to guide the cluster discovery in attributed network [5], [6], [7], [8]. T o handle the challenges, many network embedding and graph neural network related methods have been developed recently for node representation learning to improve the accuracy of downstream applications like graph classification, link prediction and graph clustering.