Research map / Graph learning / Graph self-supervised learning
Non-IID Transfer Learning on Graphs
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Graph Subtree Discrepancy to align source and target graph representations | GRADE-N · Airport node classification | accuracy | Helped |
| Use Graph Subtree Discrepancy for cross-network regression | GRADE-N · Agriculture M → MU | MAE: 0.449 -> 0.353 | Helped |
| Use Graph Subtree Discrepancy for cross-domain recommendation | GRADE-R · Amazon disjoint users: Book → Movie | NDCG@10: 0.167 -> 0.216 | Helped |
| Add node-degree features to Graph Subtree Discrepancy (ablation) | GRADE-N · Social network node classification | accuracy | Helped |
| Add target pseudo-labels to Graph Subtree Discrepancy (ablation) | GRADE-N · Social network node classification | accuracy | Helped |
| Vary the base discrepancy measure (ablation) | GRADE-N · Social network node classification | accuracy | Mixed |
| Vary the base graph neural network architecture (ablation) | GRADE-N · Social network node classification | accuracy | Mixed |
| Set the domain-discrepancy weight λ within (0.01, 0.03) (ablation) | GRADE-N · Social network node classification | accuracy | Helped |
Closest papers by meaning
- How Much Can Transfer? BRIDGE: Bounded Multi-Domain Graph Foundation Model with Generalization Guarantees (ICML 2025 · similarity 0.89)
- EvoluNet: Advancing Dynamic Non-IID Transfer Learning on Graphs (ICML 2024 · similarity 0.87)
- Harnessing Language Model for Cross-Heterogeneity Graph Knowledge Transfer (AAAI 2025 · similarity 0.87)
- Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Trees (ICML 2025 · similarity 0.86)
- Contextual Structure Knowledge Transfer for Graph Neural Networks (AAAI 2025 · similarity 0.86)
- On the Benefits of Attribute-Driven Graph Domain Adaptation (ICLR 2025 · similarity 0.86)
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