Beyond Homophily: Graph Contrastive Learning with Macro-Micro Message Passing

Abstract

Graph contrastive learning (GCL) has drawn much research attention for its ability to learn node representations in a self-supervised manner. However, the homophily assumption inherent in GNN encoders limits the direction (macro-level) and the process (micro-level) of message passing in current GCL frameworks, impairing the expressive power of GCL in non-homophilous graphs. This paper presents a novel framework that employs Macro and Micro Message Passing in GCL (M3P-GCL) to overcome these limitations and advance performance in both homophilous and non-homophilous graphs. Specifically, at the macro-level, we integrate structural and attribute views to enhance the direction of message passing, and employ an Aligned Priority-Supporting View Encoding (APS-VE) strategy to facilitate contrastive training; at the micro-level, we propose an Adaptive Self-Propagation (ASP) strategy based on role segmentation of self-loops to diversify the process of message passing in the encoder. These enhancements effectively address the limitations imposed by the homophily assumption. Experiments demonstrate that M3P-GCL outperforms both supervised and unsupervised baselines in the node classification task on various datasets with different levels of homophily.