Research map / Graph learning / GNN architectures
Adaptive Sampling Towards Fast Graph Representation Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use adaptive layer-wise sampling with explicit variance reduction instead of IID layer sampling | GCN · Cora | accuracy: 0.8506\pm0.0048 -> 0.8744\pm0.0034 | Helped |
| use adaptive layer-wise sampling with explicit variance reduction instead of IID layer sampling | GCN · Citeseer | accuracy: 0.7387\pm0.0078 -> 0.7966\pm0.0018 | Helped |
| use adaptive layer-wise sampling with explicit variance reduction instead of IID layer sampling | GCN · Pubmed | accuracy: 0.8200\pm0.0114 -> 0.9060\pm0.0016 | Helped |
| use adaptive layer-wise sampling with explicit variance reduction instead of IID layer sampling | GCN · Reddit | accuracy: 0.8611\pm0.0437 -> 0.9627\pm0.0032 | Helped |
| add explicit variance reduction to adaptive sampling (ablation) | Adapt · Cora | accuracy: 0.8588\pm0.0062 -> 0.8744\pm0.0034 | Helped |
| add explicit variance reduction to adaptive sampling (ablation) | Adapt · Reddit | accuracy: 0.9501\pm0.0047 -> 0.9627\pm0.0032 | Helped |
| add explicit variance reduction to adaptive sampling (ablation) | Adapt · Citeseer | accuracy: 0.7942\pm0.0022 -> 0.7966\pm0.0018 | No clear effect |
| add explicit variance reduction to adaptive sampling (ablation) | Adapt · Pubmed | accuracy: 0.9060\pm0.0024 -> 0.9060\pm0.0016 | No clear effect |
| add a skip connection between input and top layers (ablation) | Adapt · Cora | accuracy: 0.8744\pm0.0034 -> 0.8774\pm0.0032 | Helped |
| add a skip connection between input and top layers (ablation) | Adapt · Cora | convergence epochs: around 150 to 100 | Helped |
| add explicit 2-hop neighborhood sampling using the adjacency matrix 2-order power expansion (ablation) | Adapt · Cora | accuracy: 0.8744\pm0.0034 -> 0.8814\pm0.0017 | Helped |
| also minimize variance in the first hidden layer, in addition to the top layer (ablation) | Adapt · Cora | accuracy: 0.8744\pm0.0034 -> 0.8780\pm0.0014 | Helped |
Closest papers by meaning
- GraphSAINT: Graph Sampling Based Inductive Learning Method (ICLR 2020 · similarity 0.91)
- Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks (NeurIPS 2019 · similarity 0.91)
- Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling (ICML 2022 · similarity 0.91)
- L2-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks (CVPR 2020 · similarity 0.91)
- Simplifying Graph Convolutional Networks (ICML 2019 · similarity 0.91)
- Bandit Samplers for Training Graph Neural Networks (NeurIPS 2020 · similarity 0.90)
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