Research map / Graph learning / GNN architectures
L2-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Train GCN layers sequentially, separating feature aggregation and feature transformation | GCN · Cora | Time: 6.02s -> 0.45s | Helped |
| Train GCN layers sequentially, separating feature aggregation and feature transformation | GCN · PubMed | Time: 32s -> 2.93s | Helped |
| Use a learned stopping controller for layer-wise training | L-GCN · PPI | Time: 49s -> 26s | Helped |
| Use a learned stopping controller for layer-wise training | L-GCN · PPI | F1 (%): 97.2 -> 96.8 | Hurt |
| Increase the number of L-GCN layers from 2 to 4 (ablation) | L-GCN · PPI | F1 (%): 93.7 -> 97.7 | Helped |
| Increase the number of L-GCN layers from 2 to 4 (ablation) | L-GCN · Reddit | F1 (%): 93.8 -> 94.5 | Helped |
| Increase the number of L-GCN layers from 2 to 4 (ablation) | L-GCN · Amazon-3M | F1 (%): 88.4 -> 89.2 | Helped |
| Increase L-GCN epochs across configurations (ablation) | L-GCN · Cora | F1 (%): 83.2; 84.7; 83.0 | Mixed |
| Increase the number of L-GCN epochs from Config1 to Config3 (ablation) | L-GCN · PubMed | F1 (%): 86.8 -> 86.4 | Hurt |
| Reuse the Amazon-3M-trained controller on smaller datasets (ablation) | L²-GCN · Cora | F1 (%): 84.1 -> 84.8 | Helped |
| Apply layer-wise training instead of conventional training (ablation) | N-GCN · Cora | Time: 62s -> 4s | Helped |
| Apply layer-wise training instead of conventional training (ablation) | N-GCN · Cora | F1 (%): 83.6 -> 83.1 | Hurt |
Closest papers by meaning
- Deeper Insights Into Graph Convolutional Networks for Semi-Supervised Learning (AAAI 2018 · similarity 0.95)
- Simplifying Graph Convolutional Networks (ICML 2019 · similarity 0.94)
- PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication (ICLR 2022 · similarity 0.94)
- Simple and Deep Graph Convolutional Networks (ICML 2020 · similarity 0.93)
- On Provable Benefits of Depth in Training Graph Convolutional Networks (NeurIPS 2021 · similarity 0.92)
- On the Stability of Graph Convolutional Neural Networks: A Probabilistic Perspective (NeurIPS 2025 · similarity 0.91)
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