Research map / Graph learning / Graph self-supervised learning
Contrastive Learning Meets Homophily: Two Birds with One Stone
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace one-to-one augmented-view positive sampling with parameterized neighbor sampling plus homophily discrimination (NeCo) | GCN encoder, GCL · Cora, Citeseer, Pubmed, DBLP | accuracy | Helped |
| replace one-to-one augmented-view positive sampling with parameterized neighbor sampling plus homophily discrimination (NeCo) | GCN encoder, GCL · Cornell, Texas, Wisconsin, Actor, chameleon | accuracy | Helped |
| migrate AD-GCL min-max augmenter to neighbor contrastive framework (AD-NeCo) | GCN encoder, GCL · Cora, Citeseer, Pubmed, DBLP | accuracy | Hurt |
| use learned NeCo topology for GraphSAGE message passing | GraphSAGE · Cornell, Texas, Wisconsin, Actor, chameleon | accuracy | Helped |
| set GRACE-style temperature sensitivity: neighbor sampling vs one-to-one sampling across tau in {0.1..5} (ablation) | NeCo vs GRACE · Cora | accuracy | Helped |
| increase edge drop ratio p in one-to-one GRACE augmentation (ablation) | GRACE · Cora | accuracy | Hurt |
Closest papers by meaning
- Beyond Homophily: Graph Contrastive Learning with Macro-Micro Message Passing (AAAI 2025 · similarity 0.93)
- S3GCL: Spectral, Swift, Spatial Graph Contrastive Learning (ICML 2024 · similarity 0.92)
- Does GCL Need a Large Number of Negative Samples? Enhancing Graph Contrastive Learning with Effective and Efficient Negative Sampling (AAAI 2025 · similarity 0.92)
- Improving Graph Contrastive Learning via Adaptive Positive Sampling (CVPR 2024 · similarity 0.91)
- ReGCL: Rethinking Message Passing in Graph Contrastive Learning (AAAI 2024 · similarity 0.90)
- Efficient Contrastive Learning for Fast and Accurate Inference on Graphs (ICML 2024 · similarity 0.89)
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