Node Classification On Pattern 100K
Métriques
Accuracy (%)
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | Accuracy (%) |
|---|---|
| directional-graph-networks-1 | 86.680 |
| inductive-representation-learning-on-large | 50.516 |
| edge-augmented-graph-transformers-global-self | 86.816 |
| principal-neighbourhood-aggregation-for-graph | 86.567 |
| factorizable-graph-convolutional-networks | 86.57 ± 0.02 |
| residual-gated-graph-convnets | 84.480 |
| graph-attention-networks | 75.824 |
| how-powerful-are-graph-neural-networks | 85.590 |
| geometric-deep-learning-on-graphs-and | 85.482 |