Exploiting Composite Relation Graph Convolution for Attributed Network Embedding

Network embedding aims at learning a low-dimensional dense vector for each node in the network. It has attracted much attention from researchers in recent years. Most existing studies mainly focus on modeling graph structure and neglect the attribute information. Though attributed network embedding methods take node attribute into account, the informative relations between nodes and their attributes are still under-exploited. In this paper, we propose a novel framework to employ the abundant relation information for attributed network embedding. To this end, we first present to construct the composite relations between the nodes and their attributes in attributed networks. We then develop a composite relation graph convolution network (CRGCN) to encode the composite relations in both types of networks. We conduct extensive experiments on real world datasets and results demonstrate the effectiveness of our model on various network analysis tasks.