Graph Classification on Mutagenicity
98.8AccuracyCDAT
Evaluation results
| Method | Links | |
|---|---|---|
| CDATevaluation_protocol=100 runs of 10-fold cross-validation2026.06 | 98.8 | |
| ETevaluation_protocol=100 runs of 10-fold cross-validation2026.06 | 98.7 | |
| U2GNNevaluation_protocol=100 runs of 10-fold cross-validation2026.06 | 88.5 | |
| TopKPool2026.05 | 82.45 | |
| HGP-SLevaluation_protocol=100 runs of 10-fold cross-validation2026.06 | 82.2 | |
| HMH2026.05 | 81.7 | |
| BN-PoolPooler=BN-Pool2025.01 | 81 | |
| DiffPool2026.05 | 80.78 | |
| SCNModel Architecture=Subgraph Concept Network2026.04 | 80.53 | |
| MaxCut-Pool2026.05 | 80.34 | |
| gPool2026.05 | 80.3 | |
| EigenPool2026.05 | 80.11 | |
| GraclusPooler=Graclus2025.01 | 80 | |
| ECPoolPooler=ECPool2025.01 | 80 | |
| SEPPooler=SEP2025.01 | 80 | |
| DMoNPooler=DMoN2025.01 | 80 | |
| JBGNNPooler=JBGNN2025.01 | 80 | |
| HOSCPooler=HOSC2025.01 | 80 | |
| SAGPool(G)2026.05 | 79.72 | |
| CGNModel Architecture=Concept Graph Network, Pooling Method=DiffPool2026.04 | 79.67 | |
| Mincut-Pool2026.05 | 79.34 | |
| SEP2026.05 | 79.1 | |
| k-MISPooler=k-MIS2025.01 | 79 | |
| EigenPooler=Eigen2025.01 | 79 | |
| CGNModel Architecture=Concept Graph Network, Pooling Method=mean pool2026.04 | 78.77 | |
| GIPModel Architecture=GIP2026.04 | 78.42 | |
| -Pooler=-2025.01 | 78 | |
| DiffPoolPooler=DiffPool2025.01 | 78 | |
| MinCutPooler=MinCut2025.01 | 78 | |
| HGPevaluation_protocol=100 runs of 10-fold cross-validation2026.06 | 77.9 | |
| Pure CGC2026.04 | 77.67 | |
| CGC2026.04 | 77.4 | |
| GAT2026.04 | 76.59 | |
| GCN2026.04 | 75.58 | |
| Top-kPooler=Top-k2025.01 | 75 |