Research map / Graph learning / GNN architectures
On Provable Benefits of Depth in Training Graph Convolutional Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| decouple weight matrices from propagation (DGCN) versus plain GCN | GCN · OGB Products | F1 (%): 75.39 -> 76.09 | Helped |
| DGCN with per-layer weights versus APPNP shared single weight | APPNP · OGB Products | F1 (%): 66.35 -> 76.09 | Helped |
| DGCN versus ResGCN | ResGCN · OGB Arxiv | F1 (%): 72.56 -> 72.63 | No clear effect |
| DGCN versus GCNII without manual tuning | GCNII · OGB Proteins | F1 (%): 75.60 -> 75.45 | No clear effect |
| add skip-connections (ResGCN) to deeper GCN | GCN · synthetic CSBM | generalization gap | Hurt |
| decouple weights from propagation (APPNP) versus GCN | GCN · synthetic CSBM | generalization gap | Helped |
Closest papers by meaning
- Simple and Deep Graph Convolutional Networks (ICML 2020 · similarity 0.93)
- Deeper Insights Into Graph Convolutional Networks for Semi-Supervised Learning (AAAI 2018 · similarity 0.93)
- Graph Neural Networks Do Not Always Oversmooth (NeurIPS 2024 · similarity 0.91)
- A Manifold Perspective on the Statistical Generalization of Graph Neural Networks (ICML 2025 · similarity 0.90)
- On the Stability of Graph Convolutional Neural Networks: A Probabilistic Perspective (NeurIPS 2025 · similarity 0.90)
- On Which Nodes Does GCN Fail? Enhancing GCN From the Node Perspective (ICML 2024 · similarity 0.90)
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