Graph Classification On Malnet Tiny
Metriken
Accuracy
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Vergleichstabelle
| Modellname | Accuracy |
|---|---|
| exphormer-sparse-transformers-for-graphs | 94.02±0.209 |
| unlocking-the-potential-of-classic-gnns-for | 94.600±0.570 |
| masked-attention-is-all-you-need-for-graphs | 94.800±0.424 |
| recipe-for-a-general-powerful-scalable-graph | 93.36 ± 0.6 |