Papers › Deep Graph Contrastive Representation Learning
Deep Graph Contrastive Representation Learning
Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, Liang Wang
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.
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Tasks
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Node Classification | Citeseer | GRACE | Accuracy | 72.1 ± 0.5 | #45 of 71 | Archive leaderboard | report |
| Node Classification | Cora | GRACE | Accuracy | 83.3% ± 0.4% | #42 of 73 | Archive leaderboard | report |
| Node Classification | DBLP | GRACE | Accuracy | 84.2 ± 0.1 | #1 of 6 | Archive leaderboard | report |
| Node Classification | PPI | GRACE | F1 | 66.2 | #20 of 24 | Archive leaderboard | report |
| Node Classification | PPI | GRACE | Micro-F1 | 66.2 | #20 of 24 | Archive leaderboard | report |
| Node Classification | Pubmed | GRACE | Accuracy | 86.7 ± 0.1 | #19 of 70 | Archive leaderboard | report |
| Node Classification | GRACE | Micro-F1 | 94.2 ± 0.0 | #16 of 16 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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