Graph Classification on REDDIT BINARY
92.6AccuracyRetGk
Evaluation results
| Method | Links | ||
|---|---|---|---|
| RetGk2018.11 | 92.6 | — | |
| GIN2018.11 | 92.4 | — | |
| DiffPoolPooling Strategy=Hierarchical2019.03 | 92.1 | — | |
| LDP2018.11 | 92.1 | — | |
| LDPClassifier=SVM2018.11 | 92.1 | — | |
| KANbasis functions=fixed, tuned, backbone=GIN2025.07 | 91.7 | — | |
| R-GIN + PANDABackbone=R-GIN, Rewiring Method=PANDA2024.06 | 91.36 | — | |
| SCNModel Architecture=Subgraph Concept Network2026.04 | 91.23 | — | |
| δ-2-LWL+Type=Local Kernel2019.04 | 91.1 | — | |
| PANDABackbone=GIN2024.06 | 91.055 | — | |
| CORE-SP2024.06 | 91 | — | |
| CORE-SPMethod Category=Kernels2025.10 | 91 | — | |
| CGNModel Architecture=Concept Graph Network, Pooling Method=mean pool2026.04 | 90.49 | — | |
| R-GIN + GTRBackbone=R-GIN, Rewiring Method=GTR2024.06 | 90.41 | — | |
| GIN-εPooling Strategy=Flat2019.03 | 90.3 | — | |
| Gin-εType=Neural2019.04 | 90.3 | — | |
| Last Layer FABackbone=GIN2024.06 | 90.22 | — | |
| R-GIN + Last Layer FABackbone=R-GIN, Rewiring Method=Last Layer FA2024.06 | 89.995 | — | |
| MLPbackbone=GIN2025.07 | 89.9 | — | |
| GINMethod Category=GNNs2025.10 | 89.9 | — | |
| CGNModel Architecture=Concept Graph Network, Pooling Method=DiffPool2026.04 | 89.88 | — | |
| Gin-0Type=Neural2019.04 | 89.8 | — | |
| LDPEvaluation Protocol=linear SVM2018.11 | 89.8 | — | |
| LDPClassifier=Linear SVM2018.11 | 89.8 | — | |
| δ-2-LWLType=Local Kernel2019.04 | 89.7 | — | |
| R-GIN + FOSRBackbone=R-GIN, Rewiring Method=FOSR2024.06 | 89.665 | — | |
| GIN-0Pooling Strategy=Flat2019.03 | 89.6 | — | |
| Set2SetPooling Strategy=Global2019.03 | 89.6 | — | |
| DYF2018.11 | 89.51 | — | |
| ML2024.06 | 89.4 | — | |
| MLMethod Category=Kernels2025.10 | 89.4 | — | |
| GCNPooling Strategy=Flat2019.03 | 89.3 | — | |
| WL-OA2018.11 | 89.3 | — | |
| SAGEPooling Strategy=Flat2019.03 | 89.1 | — | |
| DiffPoolMethod Category=GNNs2025.10 | 89.1 | — | |
| WL-OA2024.06 | 89 | — | |
| WL-OAMethod Category=Kernels2025.10 | 89 | — | |
| FB2018.11 | 88.98 | — | |
| GIPModel Architecture=GIP2026.04 | 88.89 | — | |
| GraclusPooling Strategy=Hierarchical2019.03 | 88.8 | — | |
| Graphlet2026.05 | 88.6 | — | |
| GIC2018.11 | 88.45 | — | |
| WLOAType=Baseline2019.04 | 88.1 | — | |
| R-GINBackbone=R-GIN, Rewiring Method=None2024.06 | 87.965 | — | |
| SAGE w/o JKPooling Strategy=Global2019.03 | 87.9 | — | |
| DGCNNMethod Category=GNNs2025.10 | 87.8 | — | |
| GCAPS-CNNcategory=Deep Learning Method2018.05 | 87.61 | — | |
| topkPooling Strategy=Hierarchical2019.03 | 87.6 | — | |
| CGC2026.04 | 87.55 | — | |
| GlobalAttentionPooling Strategy=Global2019.03 | 87.4 | — | |
| FOSRBackbone=GIN2024.06 | 87.35 | — | |
| GTRBackbone=GIN2024.06 | 86.98 | — | |
| R-GIN + SDRFBackbone=R-GIN, Rewiring Method=SDRF2024.06 | 86.825 | — | |
| GINRewiring=None2024.06 | 86.785 | — | |
| FGSD2018.11 | 86.5 | — | |
| PM2024.06 | 86.5 | — | |
| PMMethod Category=Kernels2025.10 | 86.5 | — | |
| SDRFBackbone=GIN2024.06 | 86.44 | — | |
| PSCNcategory=Deep Learning Method2018.05 | 86.3 | — | |
| PSCN2018.11 | 86.3 | — | |
| PSCN2018.11 | 86.3 | — | |
| SAEN2018.11 | 86.08 | — | |
| DiffusionMethod Category=Diffusion Baselines2025.10 | 85.6 | — | |
| NetLSD2026.05 | 85.3 | — | |
| Pure CGC2026.04 | 84.75 | — | |
| SPType=Baseline2019.04 | 84.6 | — | |
| GRF++Method Category=Diffusion Baselines, degree=22025.10 | 84.4 | — | |
| BGFI2024.06 | 84.3 | — | |
| GraphSAGEMethod Category=GNNs2025.10 | 84.3 | — | |
| GRFMethod Category=Diffusion Baselines2025.10 | 84 | — | |
| FTFI2024.06 | 83.7 | — | |
| InfinityKANbasis functions=automatically learned, backbone=GIN2025.07 | 83.6 | — | |
| WL subtree2026.05 | 82.5 | — | |
| SP2024.06 | 81.7 | — | |
| SPMethod Category=Kernels2025.10 | 81.7 | — | |
| NH2024.06 | 81.2 | — | |
| NHMethod Category=Kernels2025.10 | 81.2 | — | |
| PANDA-GCN2024.06 | 80.69 | — | |
| PANDABackbone=GCN2024.06 | 80.69 | — | |
| R-GCN + PANDABackbone=R-GCN, Rewiring Method=PANDA2024.06 | 80.2 | — | |
| R-GCN + GTRBackbone=R-GCN, Rewiring Method=GTR2024.06 | 80.18 | — | |
| DGKcategory=Graph Kernels2018.05 | 78.04 | — | |
| DGK2018.11 | 78.04 | — | |
| DGK2018.11 | 78 | — | |
| MS-SDMW2026.05 | 77.9 | — | |
| GKcategory=Graph Kernels2018.05 | 77.34 | — | |
| GK2018.11 | 77.34 | — | |
| GK2018.11 | 77.3 | — | |
| Shortest-path2026.05 | 77.2 | — | |
| GPF-LoRAPQuantization Framework=MixQ2026.01 | 76.6 | 5.7 | |
| R-GCN + FOSRBackbone=R-GCN, Rewiring Method=FOSR2024.06 | 76.59 | — | |
| GR2024.06 | 76.1 | — | |
| GRMethod Category=Kernels2025.10 | 76.1 | — | |
| DIGLBackbone=GIN2024.06 | 76.035 | — | |
| DGCNNcategory=Deep Learning Method2018.05 | 76.02 | — | |
| LoRAPQuantization Framework=MixQ2026.01 | 75.6 | 5.7 | |
| WL2018.11 | 75.3 | — | |
| SortPoolPooling Strategy=Global2019.03 | 74.9 | — | |
| SVM-v2024.06 | 74.8 | — | |
| SVM-ϑMethod Category=Kernels2025.10 | 74.8 | — |