Graph Classification on PTC-MR
76.4AccuracyISP-GNN†
Evaluation results
| Method | Links | ||
|---|---|---|---|
| ISP-GNN†Evaluation protocol=10-fold cross-validation2026.03 | 76.4 | — | |
| KP-GINEvaluation protocol=10-fold cross-validation2026.03 | 76.2 | — | |
| ISP-GNN_OnionEvaluation protocol=10-fold cross-validation2026.03 | 75.9 | — | |
| IPR-MPNN2024.05 | 75.8 | — | |
| ISP-GNN_DegreeEvaluation protocol=10-fold cross-validation2026.03 | 75.8 | — | |
| AC-GINEvaluation protocol=10-fold cross-validation2026.03 | 75.6 | — | |
| ISP-GNN_CoreEvaluation protocol=10-fold cross-validation2026.03 | 75.5 | — | |
| UnionGINEvaluation protocol=10-fold cross-validation2026.03 | 74.8 | — | |
| ID-GNNEvaluation protocol=10-fold cross-validation2026.03 | 74.4 | — | |
| PR-MPNN2024.05 | 74.3 | — | |
| GIN-AK+Evaluation protocol=10-fold cross-validation2026.03 | 74.1 | — | |
| CIN++Type=Topological neural network2023.06 | 73.2 | — | |
| CIN++2024.05 | 73.2 | — | |
| BEC-GINEvaluation protocol=10-fold cross-validation2026.03 | 72.9 | — | |
| CANType=Topological neural network2023.06 | 72.8 | — | |
| CAN2024.05 | 72.8 | — | |
| NC-GNNEvaluation protocol=10-fold cross-validation2026.03 | 71.8 | — | |
| GRAPHORMERProtocol=10-fold cross-validation2026.03 | 71.4 | — | |
| GSNEvaluation protocol=10-fold cross-validation2026.03 | 70.6 | — | |
| GraphSNNEvaluation protocol=10-fold cross-validation2026.03 | 70.6 | — | |
| GCBM-EProtocol=10-fold cross-validation2026.03 | 70.6 | — | |
| GMTProtocol=10-fold cross-validation2026.03 | 70.2 | — | |
| GraphTrailProtocol=10-fold cross-validation2026.03 | 69.3 | — | |
| GRIT2026.05 | 69.18 | — | |
| CTQWformer2026.05 | 69.16 | — | |
| CTQWformer2026.05 | 69.16 | — | |
| SEPProtocol=10-fold cross-validation2026.03 | 68.5 | — | |
| GCBMProtocol=10-fold cross-validation2026.03 | 68.5 | — | |
| PDF2023.05 | 68.36 | — | |
| MINCUTPOOLProtocol=10-fold cross-validation2026.03 | 68.3 | — | |
| GRDLProtocol=10-fold cross-validation2026.03 | 68.3 | — | |
| SATProtocol=10-fold cross-validation2026.03 | 68.3 | — | |
| GSNType=Graph neural network2023.06 | 68.2 | — | |
| CINType=Topological neural network2023.06 | 68.2 | — | |
| GSN2024.05 | 68.2 | — | |
| CIN2024.05 | 68.2 | — | |
| ProtGNNProtocol=10-fold cross-validation2026.03 | 68.2 | — | |
| CH-CL2026.04 | 68.18 | — | |
| OT-GNNProtocol=10-fold cross-validation2026.03 | 68 | — | |
| DropGINEvaluation protocol=10-fold cross-validation2026.03 | 67.1 | — | |
| GNNExplainerProtocol=10-fold cross-validation2026.03 | 67.1 | — | |
| PGExplainerProtocol=10-fold cross-validation2026.03 | 67 | — | |
| PGIBProtocol=10-fold cross-validation2026.03 | 66.9 | — | |
| GCN22023.05 | 66.84 | — | |
| Natural GNType=Graph neural network2023.06 | 66.8 | — | |
| NATURAL GN2024.05 | 66.8 | — | |
| ConfExplainerProtocol=10-fold cross-validation2026.03 | 66.8 | — | |
| GATLearning paradigm=Supervised Approaches2021.10 | 66.7 | — | |
| GAT2026.05 | 66.7 | — | |
| Space OptimumRefinement budget=1002025.07 | 66.67 | — | |
| M-DESIGNRefinement budget=1002025.07 | 66.67 | — | |
| VGIBProtocol=10-fold cross-validation2026.03 | 66.5 | — | |
| DROPGNN2024.05 | 66.3 | — | |
| DROPGINProtocol=10-fold cross-validation2026.03 | 66.3 | — | |
| WL subtree2026.05 | 66.3 | — | |
| PPGNSType=Graph neural network2023.06 | 66.2 | — | |
| PPGNS2024.05 | 66.2 | — | |
| WEGLProtocol=10-fold cross-validation2026.03 | 66.2 | — | |
| PPGNN2023.05 | 66.17 | — | |
| CAPSGNN2026.05 | 66.01 | — | |
| WL-OA2024.06 | 65.7 | — | |
| WL-OACategory=Kernels2025.10 | 65.7 | — | |
| GINEvaluation protocol=10-fold cross-validation2026.03 | 65.6 | — | |
| CORE-WL-VH2024.06 | 65.5 | — | |
| CORE-WL-VHCategory=Kernels2025.10 | 65.5 | — | |
| SGFormerMethod Category=Graph Transformers2025.08 | 65.2 | — | |
| Graphormer2026.05 | 65.12 | — | |
| WL-PM2024.06 | 65.1 | — | |
| PK2024.06 | 65.1 | — | |
| WL-PMCategory=Kernels2025.10 | 65.1 | — | |
| PKCategory=Kernels2025.10 | 65.1 | — | |
| WL-VH2024.06 | 64.9 | — | |
| WL-VHCategory=Kernels2025.10 | 64.9 | — | |
| GIN-0Learning paradigm=Supervised Approaches2021.10 | 64.6 | — | |
| GIN2023.05 | 64.6 | — | |
| GINType=Graph neural network2023.06 | 64.6 | — | |
| GIN2024.05 | 64.6 | — | |
| GINProtocol=10-fold cross-validation2026.03 | 64.6 | — | |
| ASAPProtocol=10-fold cross-validation2026.03 | 64.6 | — | |
| WITTOPOPOOLProtocol=10-fold cross-validation2026.03 | 64.6 | — | |
| GIN-02026.05 | 64.6 | — | |
| ML2024.06 | 64.5 | — | |
| MLCategory=Kernels2025.10 | 64.5 | — | |
| GCNLearning paradigm=Supervised Approaches2021.10 | 64.2 | — | |
| GraphSAGELearning paradigm=Supervised Approaches2021.10 | 63.9 | — | |
| GraphSAGE2026.05 | 63.9 | — | |
| GIN-eLearning paradigm=Supervised Approaches2021.10 | 63.7 | — | |
| WL-OAKernel Type=Weisfeiler-Lehman Optimal Assignment2016.06 | 63.6 | — | |
| WL-OAMethod category=Kernel2018.10 | 63.6 | — | |
| InfoGCLLearning paradigm=Unsupervised Approaches2021.10 | 63.5 | — | |
| TopoGCL2026.04 | 63.43 | — | |
| NH2024.06 | 63.4 | — | |
| NHCategory=Kernels2025.10 | 63.4 | — | |
| MLGLearning paradigm=Kernel Approaches2021.10 | 63.3 | — | |
| MLGLearning Paradigm=Graph Kernel2019.07 | 63.26 | — | |
| AD-GCL2026.04 | 63.2 | — | |
| AutoGCL2026.04 | 63.1 | — | |
| GCNMethod Category=GNN methods2025.08 | 63.1 | — | |
| GCL-SPAN2026.04 | 62.86 | — | |
| FGSD2023.05 | 62.8 | — |