Graph Contrastive Learning with Structure Information

Abstract

Recently, many researches for node representation learning have focused on contrastive learning. Node-level contrastive learning aims to distinguish the same node representations (positive pairs) in two different views from other node representations (negative pairs). However, since negative pairs are sampled regardless of graph structure, most constrastive methods make no consideration of the graph homophily that similar nodes may be more likely to attach to each other than dissimilar ones. In this study, we propose two ideas that take into account the structure information of graph. 1) Edge Reconstruction Loss. It uses the representations of the proximity nodes as positive pairs. 2) Average Edge Reconstruction Loss. It uses each node representation and average of representations of the proximity nodes as positive pairs. We perform experiments with datasets which have various properties such as citation and co-selling. Experimental results show that our method improves the conventional baseline study.