Research map / Graph learning / GNN architectures
Stochastic Training of Graph Convolutional Networks with Variance Reduction
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Move dropout after graph propagation (ablation) | GCN · Citeseer, Cora, PubMed, PPI, Reddit | testing accuracy: 70.8± .1 -> 70.9± .2; 81.7± .5 -> 82.0± .8; 79.0± .4 -> 78.7± .3; 97.9± .04 -> 97.8± .05; 96.2± .04 -> 96.3± .07 | No clear effect |
| Add preprocessing to neighbor sampling (ablation) | GCN on Reddit · Reddit | time to 96% validation accuracy: 577 -> 195 | Helped |
| Use control variates and preprocessing instead of exact training | GCN on Reddit · Reddit | time to 96% validation accuracy: 252 -> 56 | Helped |
| Use dropout-aware control variates and preprocessing instead of exact training | GCN on Reddit · Reddit | time to 96% validation accuracy: 252 -> 50 | Helped |
| Use neighbor sampling without control variates | GCN · Reddit | validation accuracy: 96.0 -> 94.4 | Hurt |
| Use dropout-aware control variates rather than ordinary control variates (ablation) | GCN on Reddit · Reddit | time to 96% validation accuracy: 56 -> 50 | Helped |
Closest papers by meaning
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- L2-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks (CVPR 2020 · similarity 0.90)
- VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization (NeurIPS 2021 · similarity 0.89)
- Generalizing CNNs to graphs with learnable neighborhood quantization (NeurIPS 2024 · similarity 0.87)
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