Predictive Masking for Semi-Supervised Graph Contrastive Learning

Abstract

Graph Contrastive Learning (GCL) has recently emerged to leverage contrastive loss as a pseudo-supervision signal for self-supervised learning. In order to introduce contrastive learning loss to graphs, existing GCL methods mostly focus on leveraging network topology or node similarity to classify a pair of nodes as same/different node pairs or close/distant node pairs. In this paper, we propose a semi-supervised graph contrastive learning framework, pmGCL, leveraging GCL to augment the performance of a classifier through a predictive masking approach. Specifically, a classifier is trained using a small number of labeled nodes to predict node labels. The label prediction results are then transformed into a binary prediction of whether two nodes have the same label or not for all node pairs. The converted result, serving as a binary masking matrix, will help the succeeding GCL learning to learn to pull nodes likely belonging to the same class to be closer and push the ones belonging to different classes to be further away from each other. Experiments and comparisons, with respect to different benchmark networks and label percentages, show that pmGCL consistently outperforms rival graph convolution neural network (GCN) and GCL baseline with a simple constraint posed on the problem.