Multiplex Graph Contrastive Learning with Soft Negatives

Graph Contrastive Learning (GCL) seeks to learn nodal or graph representations that contain maximal consistent information from graph-structured data. While node-level contrasting modes are dominating, some efforts have commenced to explore consistency across different scales. Yet, they tend to lose consistent information and be contaminated by disturbing features. We propose MUX-GCL, a novel cross-scale contrastive learning framework that addresses these key challenges in GCL by leveraging multiplex representations as effective patches to enhance information consistency. Our method introduces a soft-negative contrasting strategy based on positional affinities to reduce false negatives, thereby minimizing information loss during multi-scale contrasts. While this learning mode minimizes contaminating noises, a commensurate contrasting strategy using positional affinities further avoids information loss by correcting false negative pairs across scales. Extensive downstream experiments demonstrate that MUX-GCL yields multiple state-of-the-art results on public datasets. Our theoretical analysis further guarantees the new objective function as a stricter lower bound of mutual information of raw input features and output embeddings, which rationalizes this paradigm.