Research map / Graph learning / Graph self-supervised learning
InfoGCL: Information-Aware Graph Contrastive Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add negative samples to contrastive loss (ablation) | InfoGCL · Cora | accuracy (%): 78.6 -> 83.5 | Helped |
| add negative samples to contrastive loss (ablation) | InfoGCL · Citeseer | accuracy (%): 70.4 -> 73.5 | Helped |
| add negative samples to contrastive loss (ablation) | InfoGCL · Pubmed | accuracy (%): 77.4 -> 79.1 | Helped |
| add negative samples to contrastive loss (ablation) | InfoGCL · MUTAG, IMDB-B, COLLAB | accuracy (%): 91.0 -> 91.2 (MUTAG); 75.1 -> 75.1 (IMDB-B); 80.2 -> 80.0 (COLLAB) | No clear effect |
Closest papers by meaning
- ReGCL: Rethinking Message Passing in Graph Contrastive Learning (AAAI 2024 · similarity 0.90)
- S3GCL: Spectral, Swift, Spatial Graph Contrastive Learning (ICML 2024 · similarity 0.90)
- A New Mechanism for Eliminating Implicit Conflict in Graph Contrastive Learning (AAAI 2024 · similarity 0.90)
- Does GCL Need a Large Number of Negative Samples? Enhancing Graph Contrastive Learning with Effective and Efficient Negative Sampling (AAAI 2025 · similarity 0.89)
- Efficient Contrastive Learning for Fast and Accurate Inference on Graphs (ICML 2024 · similarity 0.89)
- Improving Graph Contrastive Learning via Adaptive Positive Sampling (CVPR 2024 · similarity 0.89)
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