Research map / Graph learning / Graph self-supervised learning
Attribute and Structure Preserving Graph Contrastive Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use ASP-J with the attribute kNN view and joint contrastive objectives | SGC-based node representation learning · Cora | accuracy: 83.4±0.5 -> 84.7±0.6 | Helped |
| Use ASP-J with the attribute kNN view and joint contrastive objectives | SGC-based node representation learning · Citeseer | accuracy: 72.5±0.7 -> 73.0±1.0 | Helped |
| Use ASP-J with the attribute kNN view and joint contrastive objectives | SGC-based node representation learning · Pubmed | accuracy: 82.1±0.5 -> 80.7±0.6 | Hurt |
| Use ASP-cos with the attribute kNN view and joint contrastive objectives | SGC-based node representation learning · Cornell | accuracy: 74.3 -> 78.2 | Helped |
| Use ASP-cos with the attribute kNN view and joint contrastive objectives | SGC-based node representation learning · Texas | accuracy: 74.8 -> 81.6 | Helped |
| Use ASP-cos with the attribute kNN view and joint contrastive objectives | SGC-based node representation learning · Wisconsin | accuracy: 77.5 -> 79.4 | Helped |
| Use ASP-J with the attribute kNN view and joint contrastive objectives | SGC-based node representation learning · Actor | accuracy: 31.6 -> 33.6 | Helped |
| Add attribute-preserving contrastive learning (ablation) | ASP · Cornell | accuracy: 65.4 -> 78.2 | Helped |
| Add structure-preserving contrastive learning (ablation) | ASP · Cornell | accuracy: 68.6 -> 78.2 | Helped |
| Add cross-module loss Lcross (ablation) | ASP · Cornell | accuracy: 72.0 -> 78.2 | Helped |
Closest papers by meaning
- ReGCL: Rethinking Message Passing in Graph Contrastive Learning (AAAI 2024 · similarity 0.91)
- Beyond Homophily: Graph Contrastive Learning with Macro-Micro Message Passing (AAAI 2025 · similarity 0.91)
- S3GCL: Spectral, Swift, Spatial Graph Contrastive Learning (ICML 2024 · similarity 0.90)
- Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive Learning (AAAI 2024 · similarity 0.90)
- Graph Structure Refinement with Energy-based Contrastive Learning (AAAI 2025 · 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)
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