Research map / Graph learning / GNN architectures
DropEdge: Towards Deep Graph Convolutional Networks on Node Classification
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Apply DropEdge to IncepGCN | IncepGCN · Cora | accuracy: 87.44 -> 88.20 | Helped |
| Apply DropEdge to IncepGCN | IncepGCN · Citeseer | accuracy: 79.66 -> 80.50 | Helped |
| Apply DropEdge to GraphSAGE | GraphSAGE · Pubmed | accuracy: 90.60 -> 91.70 | Helped |
| Apply DropEdge to JKNet | JKNet · Reddit | accuracy: 96.27 -> 97.02 | Helped |
| Add DropEdge to GCN (ablation) | GCN (8 layers) · Cora | accuracy: 78.70 -> 85.80 | Helped |
| Add DropEdge to GCN (ablation) | GCN (8 layers) · Citeseer | accuracy: 74.60 -> 77.20 | Helped |
| Add DropEdge to GCN (ablation) | GCN (8 layers) · Pubmed | accuracy: 90.10 -> 90.90 | Helped |
| Add DropEdge to GCN (ablation) | GCN (8 layers) · Reddit | accuracy: 96.17 -> 96.48 | Helped |
| Use layer-wise rather than shared-adjacency DropEdge (ablation) | GCN · Cora | training loss | Helped |
| Use layer-wise rather than shared-adjacency DropEdge (ablation) | GCN · Cora | validation loss | No clear effect |
| Add Dropout alongside DropEdge (ablation) | GCN-4 · Cora | validation loss | Helped |
| Add DropEdge rather than using Dropout alone (ablation) | GCN-4 · Cora | validation loss | Helped |
Closest papers by meaning
- Towards Deepening Graph Neural Networks: A GNTK-based Optimization Perspective (ICLR 2022 · similarity 0.91)
- Beyond Random Masking: When Dropout meets Graph Convolutional Networks (ICLR 2025 · similarity 0.90)
- Effects of Dropout on Performance in Long-range Graph Learning Tasks (NeurIPS 2025 · similarity 0.89)
- Aggregation Buffer: Revisiting DropEdge with a New Parameter Block (ICML 2025 · similarity 0.89)
- Simplifying Graph Convolutional Networks (ICML 2019 · similarity 0.89)
- L2-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks (CVPR 2020 · similarity 0.89)
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