Graph Classification On Re M5K
Metriken
Accuracy
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Vergleichstabelle
| Modellname | Accuracy |
|---|---|
| dissecting-graph-neural-networks-on-graph | 49.75% |
| deep-graph-kernels | 41.27% |
| wasserstein-embedding-for-graph-learning | 55.1% |
| graph-classification-with-2d-convolutional | 52.11% |
| dissecting-graph-neural-networks-on-graph | 49.43% |
| how-powerful-are-graph-neural-networks | 57.5% |
| capsule-graph-neural-network | 52.88% |
| improving-attention-mechanism-in-graph-neural | 57.22% |