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Graph Classification On Re M5K
Métriques
Accuracy
Résultats
Résultats de performance de divers modèles sur ce benchmark
| Paper Title | ||
|---|---|---|
| GIN-0 | 57.5% | How Powerful are Graph Neural Networks? |
| GAT-GC (f-Scaled) | 57.22% | Improving Attention Mechanism in Graph Neural Networks via Cardinality Preservation |
| WEGL | 55.1% | Wasserstein Embedding for Graph Learning |
| CapsGNN | 52.88% | Capsule Graph Neural Network |
| 2D CNN | 52.11% | Graph Classification with 2D Convolutional Neural Networks |
| GFN-light | 49.75% | Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification |
| GFN | 49.43% | Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification |
| DGK | 41.27% | Deep Graph Kernels |
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