Research map / Graph learning / GNN architectures
Fast and Deep Graph Neural Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use FDGNN instead of RelNN | MUTAG graph classification · MUTAG | accuracy: 87.77_{\pm 2.48} -> 88.51_{\pm 3.77} | Helped |
| use FDGNN instead of KCNN | PTC graph classification · PTC | accuracy: 62.94_{\pm 1.69} -> 63.43_{\pm 5.35} | Helped |
| use FDGNN instead of WL | COX2 graph classification · COX2 | accuracy: 83.20_{\pm 0.20} -> 83.39_{\pm 2.88} | Helped |
| use FDGNN instead of KCNN | PROTEINS graph classification · PROTEINS | accuracy: 75.76_{\pm 0.28} -> 76.77_{\pm 2.86} | Helped |
| use FDGNN instead of WL | NCI1 graph classification · NCI1 | accuracy: 84.46_{\pm 0.45} -> 77.81_{\pm 1.62} | Hurt |
| use FDGNN instead of PGC-DGCNN | IMDB-b graph classification · IMDB-b | accuracy: 71.62_{\pm 1.22} -> 72.36_{\pm 3.63} | Helped |
| use FDGNN instead of KCNN | IMDB-m graph classification · IMDB-m | accuracy: 47.46_{\pm 0.21} -> 50.03_{\pm 1.25} | Helped |
| use FDGNN instead of KCNN | REDDIT graph classification · REDDIT | accuracy: 81.85_{\pm 0.12} -> 89.48_{\pm 1.00} | Helped |
| use FDGNN instead of PGC-DGCNN | COLLAB graph classification · COLLAB | accuracy: 75.00_{\pm 0.58} -> 74.44_{\pm 2.02} | Hurt |
| add multiple hidden layers rather than use one hidden layer (ablation) | FDGNN · MUTAG, COX2, NCI1, IMDB-b, IMDB-m, REDDIT, COLLAB | accuracy: 87.38_{\pm 6.55} -> 88.51_{\pm 3.77}; 82.41_{\pm 2.67} -> 83.39_{\pm 2.88}; 77.11_{\pm 1.52} -> 77.81_{\pm 1.62}; 71.79_ | Helped |
| add multiple hidden layers rather than use one hidden layer (ablation) | FDGNN · PTC and PROTEINS | accuracy: 63.43_{\pm 5.35} -> 63.43_{\pm 5.35}; 76.77_{\pm 2.86} -> 76.77_{\pm 2.86} | No clear effect |
| leave recurrent graph-embedding weights untrained and initialize them under the stability condition | FDGNN · graph classification benchmarks | accuracy | Helped |
Closest papers by meaning
- Recurrent Graph Neural Networks and Their Connections to Bisimulation and Logic (AAAI 2024 · similarity 0.91)
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- On Vanishing Gradients, Over-Smoothing, and Over-Squashing in GNNs: Bridging Recurrent and Graph Learning (NeurIPS 2025 · similarity 0.90)
- Graph Neural Networks Are More Than Filters: Revisiting and Benchmarking from A Spectral Perspective (ICLR 2025 · similarity 0.89)
- On the Stability of Graph Convolutional Neural Networks: A Probabilistic Perspective (NeurIPS 2025 · similarity 0.89)
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