Node Classification On Minesweeper
Métriques
AUCROC
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | AUCROC | Paper Title | Repository |
|---|---|---|---|
| GAT | 97.73 ± 0.73 | Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification | |
| GraphSAGE | 97.77 ± 0.62 | Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification | |
| Polynormer | 97.46±0.36 | Polynormer: Polynomial-Expressive Graph Transformer in Linear Time | |
| GCN | 97.86 ± 0.24 | Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification |
0 of 4 row(s) selected.