Research map / Graph learning / GNN architectures
IGLU: Efficient GCN Training via Lazy Updates
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use cached intermediate computations and lazy updates instead of neighborhood sampling | GCN training with IGLU · PPI-Large | test micro-F1: 0.975 ± 0.007 -> 0.987 ± 0.004 | Helped |
| Use cached intermediate computations and lazy updates instead of neighborhood sampling | GCN training with IGLU · Flickr | test micro-F1: 0.510 ± 0.001 -> 0.515 ± 0.001 | Helped |
| Use cached intermediate computations and lazy updates instead of neighborhood sampling | GCN training with IGLU · OGBN-Proteins | test ROC-AUC: 0.764 ± 0.009 -> 0.784 ± 0.004 | Helped |
| Use cached intermediate computations and lazy updates instead of neighborhood sampling | GCN training with IGLU · OGBN-Arxiv | test micro-F1: 0.712 ± 0.006 -> 0.718 ± 0.001 | Helped |
| Use cached intermediate computations and lazy updates instead of neighborhood sampling | GCN training with IGLU · Reddit | test micro-F1: 0.966 ± 0.003 -> 0.964 ± 0.001 | No clear effect |
| Cache incomplete gradients rather than node embeddings, using inverted update order (ablation) | IGLU · Reddit and OGBN-Proteins | validation convergence | Helped |
| Update incomplete gradients every two epochs instead of once per epoch (ablation) | IGLU · PPI-Large | validation micro-F1: 0.947 -> 0.899 | Hurt |
| Update incomplete gradients twice within an epoch instead of once per epoch (ablation) | IGLU · PPI-Large | validation micro-F1: 0.947 -> 0.708 | Hurt |
Closest papers by meaning
- L2-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks (CVPR 2020 · similarity 0.92)
- GCN meets GPU: Decoupling “When to Sample” from “How to Sample” (NeurIPS 2020 · similarity 0.91)
- PipeGCN: Efficient Full-Graph Training of Graph Convolutional Networks with Pipelined Feature Communication (ICLR 2022 · similarity 0.90)
- Deeper Insights Into Graph Convolutional Networks for Semi-Supervised Learning (AAAI 2018 · similarity 0.89)
- Simplifying Graph Convolutional Networks (ICML 2019 · similarity 0.89)
- On the Stability of Graph Convolutional Neural Networks: A Probabilistic Perspective (NeurIPS 2025 · similarity 0.86)
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