Research map / Graph learning / GNN architectures
Why Do Attributes Propagate in Graph Convolutional Neural Networks?
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add the conjugate-gradient-inspired difference term between consecutive layer representations | GCNII-style graph convolution · Cora | accuracy (%): 85.5 -> 86.1 | Helped |
| Add the conjugate-gradient-inspired difference term between consecutive layer representations | GCNII-style graph convolution · Citeseer | accuracy (%): 73.4 -> 74.3 | Helped |
| Add the conjugate-gradient-inspired difference term between consecutive layer representations | GCNII-style graph convolution · Pubmed | accuracy (%): 80.2 -> 81.1 | Helped |
| Add the conjugate-gradient-inspired difference term between consecutive layer representations | GCNII-style graph convolution · Texas | accuracy (%): 69.4 -> 71.15 | Helped |
| Add the conjugate-gradient-inspired difference term between consecutive layer representations | GCNII-style graph convolution · Cornell | accuracy (%): 74.8 -> 76.44 | Helped |
| Add the conjugate-gradient-inspired difference term between consecutive layer representations | GCNII-style graph convolution · Wisconsin | accuracy (%): 74.1 -> 75.37 | Helped |
| Add the conjugate-gradient-inspired difference term between consecutive layer representations | GCNII-style graph convolution · Chameleon | accuracy (%): 60.6 -> 61.95 | Helped |
| Add the conjugate-gradient-inspired difference term between consecutive layer representations | GCNII-style graph convolution · PPI | F1 score: 99.53 -> 99.60 | Helped |
| Use conjugate graph attention with learned propagation weights | GCNII-style graph convolution · PPI | F1 score: 99.53 -> 99.58 | Helped |
Closest papers by meaning
- Improving Graph Neural Networks with Learnable Propagation Operators (ICML 2023 · similarity 0.91)
- On Provable Benefits of Depth in Training Graph Convolutional Networks (NeurIPS 2021 · similarity 0.90)
- Graph Neural Networks Do Not Always Oversmooth (NeurIPS 2024 · similarity 0.89)
- Can Classic GNNs Be Strong Baselines for Graph-level Tasks? Simple Architectures Meet Excellence (ICML 2025 · similarity 0.88)
- On Vanishing Gradients, Over-Smoothing, and Over-Squashing in GNNs: Bridging Recurrent and Graph Learning (NeurIPS 2025 · similarity 0.88)
- On the Stability of Graph Convolutional Neural Networks: A Probabilistic Perspective (NeurIPS 2025 · similarity 0.88)
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