Research map / Graph learning / GNN architectures
Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Increase the number of labeled nodes | Three-layer GCN with graph topology sampling · Synthetic node-prediction data | testing error | Helped |
| Increase the number of neurons per layer | Three-layer GCN with graph topology sampling · Synthetic node-prediction data | testing error | Helped |
| Increase the effective adjacency matrix norm | Three-layer GCN with graph topology sampling · Synthetic node-prediction data | testing error | Hurt |
Closest papers by meaning
- Deeper Insights Into Graph Convolutional Networks for Semi-Supervised Learning (AAAI 2018 · similarity 0.93)
- Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks (NeurIPS 2019 · similarity 0.92)
- Towards Deepening Graph Neural Networks: A GNTK-based Optimization Perspective (ICLR 2022 · similarity 0.92)
- L2-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks (CVPR 2020 · similarity 0.91)
- A Manifold Perspective on the Statistical Generalization of Graph Neural Networks (ICML 2025 · similarity 0.91)
- Adaptive Sampling Towards Fast Graph Representation Learning (NeurIPS 2018 · similarity 0.91)
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