Research map / Graph learning / GNN architectures
Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion Functionals
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add a DC layer to GCNII | GCNII · Cora, L=64 | accuracy (%): 85.50 -> 86.30 | Helped |
| Add a DC layer to GRAND | GRAND · Citeseer, L=16 | accuracy (%): 75.00 -> 75.65 | Helped |
| Add a DC layer to GRAND | GRAND · Pubmed, L=8 | accuracy (%): 78.29 -> 80.21 | Helped |
| Add a DC layer to GCN | GCN · Cora, L=128 | accuracy (%): 20.80 -> 59.90 | Helped |
| Add a DC layer to GAT | GAT · Citeseer, L=128 | accuracy (%): GAT result unavailable; GAT+ 29.30 | Helped |
| Replace the integrated GAN/TV flow model with the two-step PDENet approach (ablation) | X-FlowNet · ADNI longitudinal concentration prediction | MAE | Hurt |
| Add a DC layer after every fully connected layer rather than only after selected early layers (ablation) | GNNs with DC layers · Citation-network node classification | accuracy | Hurt |
Closest papers by meaning
- A Generalized Neural Diffusion Framework on Graphs (AAAI 2024 · similarity 0.91)
- When Edge Independence Fails: Joint Graph Diffusion with Latent Sociability Priors (NeurIPS 2026 · similarity 0.90)
- Improving Graph Neural Networks by Learning Continuous Edge Directions (ICLR 2025 · similarity 0.89)
- On Vanishing Gradients, Over-Smoothing, and Over-Squashing in GNNs: Bridging Recurrent and Graph Learning (NeurIPS 2025 · similarity 0.89)
- Towards a Mechanistic Explanation of Diffusion Model Generalization (ICML 2025 · similarity 0.89)
- Making Classic GNNs Strong Baselines Across Varying Homophily: A Smoothness–Generalization Perspective (NeurIPS 2025 · similarity 0.89)
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