Research map / Graph learning / GNN architectures
Nested Graph Neural Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace rooted-subtree node representations with pooled rooted-subgraph representations | GIN · EXP | test accuracy: 50.0±0.00 -> 99.9±0.26 | Helped |
| Use nested rooted-subgraph processing | GIN · ogbg-molpcba test | AP: 27.03{ ±0.23} -> 28.32{ ±0.41} | Helped |
| Use checkpoint ensemble | Nested GIN* · ogbg-molpcba test | AP: 28.32{ ±0.41} -> 30.07{ ±0.37} | Helped |
| Use nested rooted-subgraph processing | GIN · ENZYMES | accuracy: 38.3 ±6.4 -> 29.0 ±8.0 | Hurt |
| Add Distance Encoding (ablation) | Nested 1-GNN · QM9 μ | MAE: 0.466 -> 0.428 | Helped |
| Add Distance Encoding (ablation) | Nested 1-GNN · QM9 ε_HOMO | MAE: 0.00292 -> 0.00265 | Helped |
| Add Distance Encoding (ablation) | Nested 1-GNN · QM9 C_v | MAE: 0.111 -> 0.174 | Hurt |
| Add Distance Encoding (ablation) | Nested 1-2-GNN · QM9 U_0 | MAE: 0.349 -> 0.252 | Hurt |
Closest papers by meaning
- How Powerful are Graph Neural Networks? (ICLR 2019 · similarity 0.91)
- Union Subgraph Neural Networks (AAAI 2024 · similarity 0.89)
- Exploring Consistency in Graph Representations: from Graph Kernels to Graph Neural Networks (NeurIPS 2024 · similarity 0.89)
- Subgraph Invariant Learning Towards Large-Scale Graph Node Classification (AAAI 2025 · similarity 0.88)
- Generative Graph Pattern Machine (NeurIPS 2025 · similarity 0.88)
- Can Classic GNNs Be Strong Baselines for Graph-level Tasks? Simple Architectures Meet Excellence (ICML 2025 · similarity 0.88)
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