Research map / Graph learning / Graph self-supervised learning
SAIL: Self-Augmented Graph Contrastive Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add intra-distillation to edge MI objective (ablation) | SAIL · Link prediction: Cora, Citeseer, PubMed | AUC (%): 95.6 -> 95.9; 95.8 -> 96.1; 96.1 -> 96.8 | Helped |
| Add inter-distillation to edge MI objective (ablation) | SAIL · Link prediction: Cora, Citeseer, PubMed | AUC (%): 95.6 -> 96.5; 95.8 -> 96.8; 96.1 -> 97.1 | Helped |
| Add intra- and inter-distillation (ablation) | SAIL · Link prediction: Cora, Citeseer, PubMed | AUC (%): 95.6 -> 97.3; 95.8 -> 98.3; 96.1 -> 98.4 | Helped |
| Add intra-distillation to edge MI objective (ablation) | SAIL · Node clustering: Cora, Citeseer, PubMed | NMI (%): 51.2 -> 53.4; 42.6 -> 42.8; 31.7 -> 32.1 | Helped |
| Add inter-distillation to edge MI objective (ablation) | SAIL · Node clustering: Cora, Citeseer, PubMed | NMI (%): 51.2 -> 55.6; 42.6 -> 43.5; 31.7 -> 32.8 | Helped |
| Add both distillation modules (ablation) | SAIL · Node clustering: Cora, Citeseer, PubMed | NMI (%): 51.2 -> 58.1; 42.6 -> 44.6; 31.7 -> 33.3 | Helped |
| Use self-augmented views with intra- and inter-graph distillation | single-layer GNN · Node classification across six datasets | accuracy (%) | Mixed |
Closest papers by meaning
- Eliciting Structural and Semantic Global Knowledge in Unsupervised Graph Contrastive Learning (AAAI 2023 · similarity 0.90)
- Return to Basics: Very Simple Graph Contrastive Learning via Noise Cancellation Principle (NeurIPS 2026 · similarity 0.90)
- Stage-Aware Graph Contrastive Learning with Node-oriented Mixture of Experts (AAAI 2026 · similarity 0.89)
- ReGCL: Rethinking Message Passing in Graph Contrastive Learning (AAAI 2024 · similarity 0.88)
- TopoGCL: Topological Graph Contrastive Learning (AAAI 2024 · similarity 0.88)
- S3GCL: Spectral, Swift, Spatial Graph Contrastive Learning (ICML 2024 · similarity 0.88)
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