GraphCL: Contrastive Self-Supervised Learning of Graph Representations

TL;DR

GraphCL introduces a self-supervised contrastive learning framework for graph neural networks that enhances node representations by maximizing agreement between perturbed subgraph embeddings, outperforming existing methods.

Contribution

The paper presents a novel contrastive learning approach for graphs that is general and effective for both transductive and inductive tasks, significantly improving unsupervised node classification.

Findings

Outperforms state-of-the-art in unsupervised node classification

Effective in both transductive and inductive settings

Leverages perturbations of node features and structure

Abstract

We propose Graph Contrastive Learning (GraphCL), a general framework for learning node representations in a self supervised manner. GraphCL learns node embeddings by maximizing the similarity between the representations of two randomly perturbed versions of the intrinsic features and link structure of the same node's local subgraph. We use graph neural networks to produce two representations of the same node and leverage a contrastive learning loss to maximize agreement between them. In both transductive and inductive learning setups, we demonstrate that our approach significantly outperforms the state-of-the-art in unsupervised learning on a number of node classification benchmarks.