Graph Classification on NCI109
94.3AccuracyCDAT
Evaluation results
| Method | Links | ||
|---|---|---|---|
| CDATevaluation_protocol=100 runs of 10-fold cross-validation2026.06 | 94.3 | — | |
| ETevaluation_protocol=100 runs of 10-fold cross-validation2026.06 | 90.5 | — | |
| δ-2-LWL+Type=Local Kernel2019.04 | 89.3 | — | |
| WKPI-KC2019.04 | 87.4 | — | |
| ShareGNNEvaluation Protocol=Standard eval.2026.05 | 86.8 | — | |
| IPR-MPNN2024.05 | 86.5 | — | |
| WL-OAKernel Type=Weisfeiler-Lehman Optimal Assignment2016.06 | 86.3 | — | |
| WL-OAedge types=annotated only2018.11 | 86.3 | — | |
| NCWWL2022.05 | 86.3 | — | |
| WLKernel Type=Weisfeiler-Lehman Convolution2016.06 | 85.9 | — | |
| WKPI-KM2019.04 | 85.9 | — | |
| WL2022.05 | 85.9 | — | |
| NCWalpha=1000, beta=02022.05 | 85.9 | — | |
| WKPI (k-means)evaluation_protocol=100 runs of 10-fold cross-validation2026.06 | 85.9 | — | |
| WLEvaluation Protocol=Standard eval.2026.05 | 85.5 | — | |
| WL2018.11 | 85.2 | — | |
| WLOAType=Baseline2019.04 | 85.2 | — | |
| P-WL-UC2019.04 | 85.1 | — | |
| ESA2024.02 | 85 | — | |
| PR-MPNN2024.05 | 84.6 | — | |
| WLedge types=annotated only2018.11 | 84.5 | — | |
| RetGK2019.04 | 84.5 | — | |
| CIN++Type=Topological neural network2023.06 | 84.5 | — | |
| CIN++2024.05 | 84.5 | — | |
| 1-WLType=Baseline2019.04 | 84.3 | — | |
| δ-2-LWLType=Local Kernel2019.04 | 84.2 | — | |
| GIN2024.02 | 84.2 | — | |
| sCWNrPeak GPU Memory (MB)=262026.05 | 84.1 | — | |
| CINType=Topological neural network2023.06 | 84 | — | |
| CIN2024.05 | 84 | — | |
| DropGIN2024.02 | 84 | — | |
| CINEvaluation Protocol=Standard eval.2026.05 | 84 | — | |
| PINEvaluation Protocol=Standard eval.2026.05 | 84 | — | |
| CINPeak GPU Memory (MB)=412026.05 | 84 | — | |
| GIN + GRANOLAevaluation_protocol=100 runs of 10-fold cross-validation2026.06 | 83.7 | — | |
| PDF2023.05 | 83.62 | — | |
| CANType=Topological neural network2023.06 | 83.6 | — | |
| CAN2024.05 | 83.6 | — | |
| GSN2024.05 | 83.5 | — | |
| Norm-GNevaluation_protocol=100 runs of 10-fold cross-validation2026.06 | 83.5 | — | |
| PNA2024.02 | 83.4 | — | |
| GIN + DIGRAFBackbone=GIN, Activation=DIGRAF, Adaptive=true2024.07 | 83.3 | — | |
| GCN2024.02 | 83.1 | — | |
| GATv22024.02 | 83.1 | — | |
| GCN22023.05 | 83 | — | |
| GIN + TanhBackbone=GIN, Activation=Tanh2024.07 | 83 | — | |
| GIN + MaxoutBackbone=GIN, Activation=Maxout2024.07 | 83 | — | |
| NATURAL GN2024.05 | 83 | — | |
| GIN + LeakyReLUBackbone=GIN, Activation=LeakyReLU2024.07 | 82.9 | — | |
| GIN + GeLUBackbone=GIN, Activation=GeLU2024.07 | 82.9 | — | |
| GIN + SwishBackbone=GIN, Activation=Swish2024.07 | 82.9 | — | |
| GIN + DIGRAF (W/O ADAP.)Backbone=GIN, Activation=DIGRAF, Adaptive=false2024.07 | 82.9 | — | |
| GIC2018.11 | 82.86 | — | |
| GIN + IdentityBackbone=GIN, Activation=Identity2024.07 | 82.8 | — | |
| GIN + MedianBackbone=GIN, Activation=Median2024.07 | 82.8 | — | |
| GIN + MaxBackbone=GIN, Activation=Max2024.07 | 82.7 | — | |
| GIN + ELUBackbone=GIN, Activation=ELU2024.07 | 82.6 | — | |
| GAT2024.02 | 82.6 | — | |
| WLCategory=Graph Kernel2018.05 | 82.46 | — | |
| WLAlgorithm=WL2019.02 | 82.46 | — | |
| WL2019.04 | 82.46 | — | |
| δ-3-LWLType=Local Kernel2019.04 | 82.4 | — | |
| GIN + GRELUBackbone=GIN, Activation=GRELU2024.07 | 82.4 | — | |
| GIN + GraphNormevaluation_protocol=100 runs of 10-fold cross-validation2026.06 | 82.4 | — | |
| GIN + PReLUBackbone=GIN, Activation=PReLU2024.07 | 82.3 | — | |
| PPGNN2023.05 | 82.23 | — | |
| structure2vecedge types=annotated only2018.11 | 82.2 | — | |
| PPGNSType=Graph neural network2023.06 | 82.2 | — | |
| GIN + ReLUBackbone=GIN, Activation=ReLU2024.07 | 82.2 | — | |
| PPGNS2024.05 | 82.2 | — | |
| PPGNPeak GPU Memory (MB)=14672026.05 | 82.2 | — | |
| ECCedge types=annotated only2018.11 | 82.1 | — | |
| ChebNetimplementation=re-implemented by authors, edge types=annotated only2018.11 | 82.1 | — | |
| Multigraph ChebNetedge types=annotated and learned2018.11 | 82 | — | |
| δ-3-LWL+Type=Local Kernel2019.04 | 81.9 | — | |
| DAGCN2019.04 | 81.46 | — | |
| MLGCategory=Graph Kernel2018.05 | 81.31 | — | |
| MLGAlgorithm=MLG2019.02 | 81.31 | — | |
| GIN + SigmoidBackbone=GIN, Activation=Sigmoid2024.07 | 81.2 | — | |
| GCAPS-CNN2018.05 | 81.12 | — | |
| GCAPS-CNNAlgorithm=GCAPS-CNN2019.02 | 81.12 | — | |
| CCIN2026.05 | 81.1 | — | |
| GPS2024.02 | 80.9 | — | |
| DROPGNN2024.05 | 80.8 | — | |
| HMH2026.05 | 80.7 | — | |
| HGP-SLevaluation_protocol=100 runs of 10-fold cross-validation2026.06 | 80.7 | — | |
| Deep WL kernel2017.07 | 80.32 | — | |
| DGKCategory=Graph Kernel2018.05 | 80.32 | — | |
| DGKAlgorithm=DGK2019.02 | 80.32 | — | |
| DGK2019.04 | 80.32 | — | |
| DGK2023.05 | 80.32 | — | |
| DGKedge types=annotated only2018.11 | 80.3 | — | |
| DGK2019.04 | 80.3 | — | |
| CWN2026.05 | 80.3 | — | |
| PPGNs2026.05 | 80.2 | — | |
| WL kernel2017.07 | 80.12 | — | |
| RePHINE2026.05 | 79.2 | — | |
| G3N2026.05 | 79.2 | — | |
| MSPN2026.05 | 79.1 | — | |
| FGSD2023.05 | 78.84 | — |