Relation-aware Graph Contrastive Learning

Over the past few years, graph contrastive learning (GCL) has gained great success in processing unlabeled graph-structured data, but most of the existing GCL methods are based on instance discrimination task which typically learns representations by minimizing the distance between two versions of the same instance. However, different from images, which are assumed to be independently and identically distributed, graphs present relational information among data instances, in which each instance is related to others by links. Furthermore, the relations are heterogeneous in many cases. The instance discrimination task cannot make full use of the relational information inherent in the graph-structured data. To solve the above-mentioned problems, this paper proposes a relation-aware graph contrastive learning method, called RGCL. Aiming to capture the most important heterogeneous relations in the graph, RGCL explicitly models the edges, and then pulls semantically similar pairs of edges together and pushes dissimilar ones apart with contrastive regularization. By exploiting the full potential of the relationship among nodes, RGCL overcomes the limitations of previous GCL methods based on instance discrimination. The experimental results demonstrate that the proposed method outperforms a series of graph contrastive learning frameworks on widely used benchmarks, which justifies the effectiveness of our work.