Research map / Graph learning / GNN architectures
Rethinking the Expressive Power of GNNs via Graph Biconnectivity
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add Resistance Distance to Graphormer-GD (ablation) | Graphormer-GD · Cut Vertex Detection | accuracy: 83.3%±2.7% -> 100% | Helped |
| Add Resistance Distance to Graphormer-GD (ablation) | Graphormer-GD · Cut Edge Detection | accuracy: 100% -> 100% | No clear effect |
| Use Graphormer-GD instead of the strongest listed baseline, SUN | ZINC-Subset test · ZINC-Subset test | MAE: 0.083±0.003 -> 0.081±0.009 | Helped |
| Use Graphormer-GD instead of the strongest listed baseline, URPE | ZINC-Full test · ZINC-Full test | MAE: 0.028±0.002 -> 0.025±0.004 | Helped |
Closest papers by meaning
- Beyond Weisfeiler-Lehman: A Quantitative Framework for GNN Expressiveness (ICLR 2024 · similarity 0.94)
- An Empirical Study of Realized GNN Expressiveness (ICML 2024 · similarity 0.93)
- Towards Bridging Generalization and Expressivity of Graph Neural Networks (ICLR 2025 · similarity 0.92)
- On the Rademacher Complexity of Graph Neural Networks: Unifying Expressivity and Geometry (NeurIPS 2026 · similarity 0.91)
- Weisfeiler-Leman at the margin: When more expressivity matters (ICML 2024 · similarity 0.91)
- What Expressivity Theory Misses: Message Passing Complexity for GNNs (NeurIPS 2025 · similarity 0.90)
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