Graph Classification On Bp Fmri 97
Métriques
Accuracy
F1
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | Accuracy | F1 | Paper Title | Repository |
|---|---|---|---|---|
| IsoNN | 64.9% | 69.7% | IsoNN: Isomorphic Neural Network for Graph Representation Learning and Classification | - |
| WL | 56.2% | 58.8% | - | - |
| CNN | 54.6% | 52.8% | ImageNet Classification with Deep Convolutional Neural Networks | - |
| IsoNN-fast | 62.3% | 63.2% | IsoNN: Isomorphic Neural Network for Graph Representation Learning and Classification | - |
| SDBN | 64.8% | 63.7% | Structural Deep Network Embedding | - |
| AE | 53.6% | 69.5% | - | - |
| GIN | 45.4% | 42.3% | How Powerful are Graph Neural Networks? | - |
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