Research map / Graph learning / GNN architectures
A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace end-to-end graph training with layer-wise MLP training and ensemble predictions | EnGCN · Flickr | test accuracy: 54.60 ± 0.40% -> 56.43 ± 0.21% | Helped |
| Replace end-to-end graph training with layer-wise MLP training and ensemble predictions | EnGCN · Reddit | test accuracy: 97.10 ± 0.00% -> 97.14 ± 0.03% | Helped |
| Replace end-to-end graph training with layer-wise MLP training and ensemble predictions | EnGCN · ogbn-products | test accuracy: 86.73 ± 0.08% -> 87.99 ± 0.04% | Helped |
| Add majority voting over the layer-wise learners (ablation) | EnGCN · Flickr | test accuracy: 50.56±0.80 -> 56.21±0.21 | Helped |
| Add majority voting over the layer-wise learners (ablation) | EnGCN · Reddit | test accuracy: 94.72±0.04 -> 96.66±0.06 | Helped |
| Add majority voting over the layer-wise learners (ablation) | EnGCN · ogbn-products | test accuracy: 75.64±0.33 -> 75.59±0.04 | No clear effect |
| Increase the number of trained hops from 0 to 3 (ablation) | EnGCN without majority voting · Flickr | test accuracy: 46.11±0.14 -> 50.56±0.80 | Helped |
| Increase the number of trained hops from 0 to 3 (ablation) | EnGCN without majority voting · Reddit | test accuracy: 72.63±0.09 -> 94.72±0.04 | Mixed |
| Increase the number of trained hops from 0 to 3 (ablation) | EnGCN without majority voting · ogbn-products | test accuracy: 63.42±0.08 -> 75.64±0.33 | Helped |
Closest papers by meaning
- Scalable and Effective Implicit Graph Neural Networks on Large Graphs (ICLR 2024 · similarity 0.91)
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- Can Classic GNNs Be Strong Baselines for Graph-level Tasks? Simple Architectures Meet Excellence (ICML 2025 · similarity 0.89)
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