Deep Graph Contrastive Representation Learning

TL;DR

This paper introduces a novel unsupervised graph representation learning framework using contrastive learning at the node level, generating diverse graph views to improve node embeddings for various tasks.

Contribution

It proposes a hybrid graph view generation scheme and provides theoretical insights from mutual information and triplet loss perspectives, outperforming existing methods.

Findings

Outperforms state-of-the-art methods on multiple datasets

Surpasses supervised methods in transductive tasks

Demonstrates strong potential for real-world applications

Abstract

Graph representation learning nowadays becomes fundamental in analyzing graph-structured data. Inspired by recent success of contrastive methods, in this paper, we propose a novel framework for unsupervised graph representation learning by leveraging a contrastive objective at the node level. Specifically, we generate two graph views by corruption and learn node representations by maximizing the agreement of node representations in these two views. To provide diverse node contexts for the contrastive objective, we propose a hybrid scheme for generating graph views on both structure and attribute levels. Besides, we provide theoretical justification behind our motivation from two perspectives, mutual information and the classical triplet loss. We perform empirical experiments on both transductive and inductive learning tasks using a variety of real-world datasets. Experimental experiments demonstrate that despite its simplicity, our proposed method consistently outperforms existing state-of-the-art methods by large margins. Moreover, our unsupervised method even surpasses its supervised counterparts on transductive tasks, demonstrating its great potential in real-world applications.