Graph Classification on REDDIT-B
93.4AccuracyCCIN
Evaluation results
| Method | Links | ||
|---|---|---|---|
| CCINRuns=9, Seed=1-92026.05 | 93.4 | — | |
| DisenSemiLearning Paradigm=Supervised/Semi-supervised2024.08 | 93.2 | — | |
| CWNRuns=9, Seed=1-92026.05 | 93.1 | — | |
| MSPNRuns=9, Seed=1-92026.05 | 92.7 | — | |
| GraphSNNRuns=9, Seed=1-92026.05 | 92.7 | — | |
| CH-CL2026.04 | 92.49 | — | |
| RNRPBackbone=GIN2026.06 | 92.42 | — | |
| GINProtocol=Unsupervised representation learning2023.06 | 92.4 | — | |
| GIN2025.12 | 92.4 | — | |
| GINLearning Paradigm=Supervised2024.08 | 92.4 | — | |
| GMCL2026.04 | 92.39 | — | |
| DiffPoolProtocol=Unsupervised representation learning2023.06 | 92.1 | — | |
| DiffPool2025.12 | 92.1 | — | |
| GraphMAE2 + COREmasking process=GraphMAE22025.12 | 92.1 | — | |
| DiffPoolLearning Paradigm=Supervised2024.08 | 92.1 | — | |
| MGNNRuns=9, Seed=1-92026.05 | 92 | — | |
| Graph-JEPAPre-training Type=Self-predictive2023.09 | 91.99 | — | |
| PXGL-GNN2025.12 | 91.84 | — | |
| GCMAE2025.12 | 91.8 | — | |
| DGCLLearning Paradigm=Unsupervised2024.08 | 91.8 | — | |
| LTP2023.05 | 91.1 | — | |
| GINRuns=9, Seed=1-92026.05 | 91.1 | — | |
| GSNRuns=9, Seed=1-92026.05 | 91.1 | — | |
| ACC2025.12 | 91 | — | |
| HOSC2025.12 | 91 | — | |
| JBPool2025.12 | 91 | — | |
| DMoN2025.12 | 91 | — | |
| Diffpool2025.12 | 91 | — | |
| ECPoolPooler=ECPool2025.01 | 91 | — | |
| BN-PoolPooler=BN-Pool2025.01 | 91 | — | |
| CI-GCL2026.04 | 90.8 | — | |
| RetGKRuns=9, Seed=1-92026.05 | 90.8 | — | |
| WLHNRuns=9, Seed=1-92026.05 | 90.7 | — | |
| HTMLRuns=9, Seed=1-92026.05 | 90.7 | — | |
| VEPMLearning Paradigm=Supervised/Semi-supervised2024.08 | 90.5 | — | |
| LaGraphPre-training Type=Self-predictive2023.09 | 90.4 | — | |
| TopoGCL2026.04 | 90.4 | — | |
| SAN2025.12 | 90.38 | — | |
| RGCL2026.04 | 90.34 | — | |
| RNRPBackbone=GCN2026.06 | 90.33 | — | |
| PANDABackbone=GIN2026.06 | 90.33 | — | |
| S2GAE2025.12 | 90.21 | — | |
| ICL2025.12 | 90.13 | — | |
| AD-GCL2026.04 | 90.07 | — | |
| ECPool2025.12 | 90 | — | |
| GraClus2025.12 | 90 | — | |
| LaPool2025.12 | 90 | — | |
| GraclusPooler=Graclus2025.01 | 90 | — | |
| k-MISPooler=k-MIS2025.01 | 90 | — | |
| SEPPooler=SEP2025.01 | 90 | — | |
| DiffPoolPooler=DiffPool2025.01 | 90 | — | |
| JBGNNPooler=JBGNN2025.01 | 90 | — | |
| HOSCPooler=HOSC2025.01 | 90 | — | |
| GIN2023.05 | 89.9 | — | |
| GCCLearning Paradigm=Unsupervised2024.08 | 89.9 | — | |
| GCCLearning Paradigm=Self-supervised2022.05 | 89.8 | — | |
| GCCProtocol=Unsupervised representation learning2023.06 | 89.8 | — | |
| GCC2025.12 | 89.8 | — | |
| LDP2023.05 | 89.6 | — | |
| SAGNN2025.12 | 89.57 | — | |
| GraphCLProtocol=Unsupervised representation learning2023.06 | 89.53 | — | |
| GraphCL2026.04 | 89.53 | — | |
| GraphCLLearning Paradigm=Unsupervised2024.08 | 89.53 | — | |
| GraphCL2025.12 | 89.5 | — | |
| GraphMAE + COREmasking process=GraphMAE2025.12 | 89.5 | — | |
| G3NRuns=9, Seed=1-92026.05 | 89.4 | — | |
| S2GAEApproach Category=Structure-based2024.10 | 89.4 | — | |
| MaskGAEApproach Category=Structure-based2024.10 | 89.4 | — | |
| DDMProtocol=Unsupervised representation learning2023.06 | 89.15 | — | |
| DiffPool2023.05 | 89.1 | — | |
| EigenPooler=Eigen2025.01 | 89 | — | |
| GraphMAE22025.12 | 88.8 | — | |
| GraphMAEApproach Category=Feature-based2024.10 | 88.8 | — | |
| GraphMAE2Approach Category=Feature-based2024.10 | 88.6 | — | |
| AutoGCL2026.04 | 88.58 | — | |
| AutoGCLLearning Paradigm=Unsupervised2024.08 | 88.58 | — | |
| GRACEApproach Category=Standard GCL2024.10 | 88.5 | — | |
| SeeGeraApproach Category=Feature-based2024.10 | 88.5 | — | |
| lrGAE 6Approach Category=Structure-based2024.10 | 88.5 | — | |
| SubGNN2025.12 | 88.47 | — | |
| lrGAE 8Approach Category=Structure-based2024.10 | 88.4 | — | |
| AUG-MAEApproach Category=Feature-based2024.10 | 88.3 | — | |
| DiGGRLearning Paradigm=Unsupervised2024.08 | 88.19 | — | |
| GraphMAEProtocol=Unsupervised representation learning2023.06 | 88.01 | — | |
| GraphMAEPre-training Type=Generative2023.09 | 88.01 | — | |
| GraphMAELearning Paradigm=Unsupervised2024.08 | 88.01 | — | |
| AUG-MAE2025.12 | 88 | — | |
| GraphMAE2025.12 | 88 | — | |
| BNPool2025.12 | 88 | — | |
| NDP2025.12 | 88 | — | |
| DMoNPooler=DMoN2025.01 | 88 | — | |
| S2GAEPre-training Type=Generative2023.09 | 87.83 | — | |
| DGCNN2023.05 | 87.8 | — | |
| lrGAE 7Approach Category=Structure-based2024.10 | 87.8 | — | |
| BGRLApproach Category=Standard GCL2024.10 | 87.3 | — | |
| PANDABackbone=GCN2026.06 | 87.28 | — | |
| ASAP2025.12 | 87 | — | |
| MinCut2025.12 | 87 | — | |
| k-MIS2025.12 | 87 | — | |
| MinCutPooler=MinCut2025.01 | 87 | — |