Research map / Graph learning / GNN architectures
Understanding Oversquashing in GNNs through the Lens of Effective Resistance
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use GTR rewiring instead of FoSR | R-GCN · Mutag, Proteins, Enzymes, Reddit-Binary, IMDB-Binary, Collab | accuracy | Helped |
| Use GTR rewiring instead of FoSR | GIN and R-GIN · Graph classification datasets | accuracy | Mixed |
| Add edges selected to reduce total effective resistance | Graph neural networks · Graph classification datasets | accuracy | Helped |
| Vary the number of added edges rather than using one fixed count (ablation) | R-GIN · Graph classification datasets | accuracy | Mixed |
| Add more edges beyond the initially helpful edges (ablation) | R-GIN · Proteins and IMDB-Binary | accuracy | No clear effect |
| Increase the number of added edges (ablation) | R-GIN · Enzymes | accuracy | Helped |
| Increase hidden dimension (ablation) | R-GIN · Graph classification datasets | accuracy | Helped |
| Combine rewiring with increased hidden dimension (ablation) | R-GIN · Graph classification datasets | accuracy | Helped |
| Use GTR rather than rewiring at every layer with full adjacency | Graph neural networks · Graph classification datasets | accuracy | Mixed |
Closest papers by meaning
- Mitigating Over-squashing without Rewiring: A Sheaf Effective Resistance Perspective (NeurIPS 2026 · similarity 0.90)
- Understanding Virtual Nodes: Oversquashing and Node Heterogeneity (ICLR 2025 · similarity 0.89)
- On Oversquashing in Graph Neural Networks Through the Lens of Dynamical Systems (AAAI 2025 · similarity 0.89)
- Graph Neural Networks Do Not Always Oversmooth (NeurIPS 2024 · similarity 0.89)
- PANDA: Expanded Width-Aware Message Passing Beyond Rewiring (ICML 2024 · similarity 0.89)
- A Signed Graph Approach to Understanding and Mitigating Oversmoothing (NeurIPS 2025 · similarity 0.89)
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