Node Classification on Computers
93.78Mean AccuracyGCN
Evaluation results
| Method | Links | |||||
|---|---|---|---|---|---|---|
| GCNSetting=Transductive2024.05 | 93.78 | — | — | — | — | |
| Polynormer-ractivation=ReLU2024.03 | 93.68 | — | — | — | — | |
| GATSetting=Transductive2024.05 | 93.53 | — | — | — | — | |
| NIDGCNSetting=Transductive2024.05 | 93.41 | — | — | — | — | |
| NIDGATSetting=Transductive2024.05 | 93.38 | — | — | — | — | |
| Polynormer2024.03 | 93.18 | — | — | — | — | |
| PolynormerSetting=Transductive2024.05 | 93.18 | — | — | — | — | |
| CNAArchitecture=TransformerConv2024.12 | 92.68 | — | — | — | — | |
| SGFormerSetting=Transductive2024.05 | 92.42 | — | — | — | — | |
| OrderedGNN2024.03 | 92.03 | — | — | — | — | |
| DIFFormer2024.03 | 91.99 | — | — | — | — | |
| GGCN2024.03 | 91.81 | — | — | — | — | |
| STAGNN2023.10 | 91.72 | — | — | — | — | |
| Exphormer2024.03 | 91.47 | — | — | — | — | |
| ExphormerArchitecture=Exphormer2024.12 | 91.47 | — | — | — | — | |
| CGT2023.12 | 91.45 | — | — | — | — | |
| NAGphormer2023.10 | 91.22 | — | — | — | — | |
| NAGphormer2024.03 | 91.22 | — | — | — | — | |
| GraphSAGE2024.03 | 91.2 | — | — | — | — | |
| GraphGPS2024.03 | 91.19 | — | — | — | — | |
| GT2023.10 | 91.18 | — | — | — | — | |
| GCNII2024.03 | 91.04 | — | — | — | — | |
| GOAT2024.03 | 90.96 | — | — | — | — | |
| GAT2023.10 | 90.78 | — | — | — | — | |
| GAT2024.03 | 90.78 | — | — | — | — | |
| Graph-MLPSetting=Transductive2024.05 | 90.78 | — | — | — | — | |
| GT2023.12 | 90.53 | — | — | — | — | |
| RCLBackbone=GraphSAGE2023.10 | 90.47 | — | — | — | — | |
| MbaGCNNumber of Layers=42025.01 | 90.39 | — | — | — | — | |
| GGCNNumber of Layers=22025.01 | 90.36 | — | — | — | — | |
| SAN2023.12 | 90.3 | — | — | — | — | |
| VQGraphSetting=Transductive2024.05 | 90.28 | — | — | — | — | |
| RCLBackbone=GCN2023.10 | 90.23 | — | — | — | — | |
| APPNP2023.10 | 90.18 | — | — | — | — | |
| APPNP2024.03 | 90.18 | — | — | — | — | |
| APPNPSetting=Transductive2024.05 | 90.18 | — | — | — | — | |
| GAT2023.12 | 90.06 | — | — | — | — | |
| SAN2023.10 | 89.83 | — | — | — | — | |
| RCLBackbone=GIN2023.10 | 89.76 | — | — | — | — | |
| GCN2023.10 | 89.65 | — | — | — | — | |
| GCN2024.03 | 89.65 | — | — | — | — | |
| M-DESIGN2025.07 | 89.59 | — | — | — | — | |
| CLNodeBackbone=GraphSAGE2023.10 | 89.57 | — | — | — | — | |
| BP2024.06 | 89.48 | — | — | — | — | |
| GCN2023.12 | 89.47 | — | — | — | — | |
| Sage2023.12 | 89.47 | — | — | — | — | |
| ProGNNBackbone=GraphSAGE2023.10 | 89.34 | — | — | — | — | |
| GPRGNN2023.10 | 89.32 | — | — | — | — | |
| GPRGNN2024.03 | 89.32 | — | — | — | — | |
| GPRGNNSetting=Transductive2024.05 | 89.32 | — | — | — | — | |
| CLNodeBackbone=GCN2023.10 | 89.28 | — | — | — | — | |
| MSGArchitecture Type=SNN-R2024.10 | 89.27 | — | — | — | — | |
| κ-GCNArchitecture Type=ANN-R2024.10 | 89.2 | — | — | — | — | |
| SpikeGCLArchitecture Type=SNN-E2024.10 | 89.04 | — | — | — | — | |
| ProGNNBackbone=GCN2023.10 | 88.72 | — | — | — | — | |
| HGCNArchitecture Type=ANN-R2024.10 | 88.71 | — | — | — | — | |
| PPRGO2024.03 | 88.69 | — | — | — | — | |
| NeuralSparseBackbone=GCN2023.10 | 88.62 | — | — | — | — | |
| GraphSAGEBackbone=GraphSAGE2023.10 | 88.62 | — | — | — | — | |
| NeuralSparseBackbone=GraphSAGE2023.10 | 88.37 | — | — | — | — | |
| GRACE2023.12 | 88.12 | — | — | — | — | |
| GCNBackbone=GCN2023.10 | 88.09 | — | — | — | — | |
| SpikeNetArchitecture Type=SNN-E2024.10 | 88 | — | — | — | — | |
| SAT2023.12 | 87.78 | — | — | — | — | |
| BernNetK=102021.06 | 87.64 | — | 0.44 | — | — | |
| ChebNet2021.06 | 87.54 | — | 0.43 | — | — | |
| PTDNetBackbone=GCN2023.10 | 87.52 | — | — | — | — | |
| GPRGNNNumber of Layers=82025.01 | 87.43 | — | — | — | — | |
| NeuralSparseBackbone=GIN2023.10 | 87.22 | — | — | — | — | |
| GRADE2023.12 | 87.17 | — | — | — | — | |
| PTDNetBackbone=GIN2023.10 | 87.08 | — | — | — | — | |
| NodeFormer2024.03 | 86.98 | — | — | — | — | |
| GINBackbone=GIN2023.10 | 86.94 | — | — | — | — | |
| SpikeGCNArchitecture Type=SNN-E2024.10 | 86.9 | — | — | — | — | |
| GPR-GNN2021.06 | 86.85 | — | 0.25 | — | — | |
| GATArchitecture Type=ANN-E2024.10 | 86.82 | — | — | — | — | |
| DFA-GNN2024.06 | 86.72 | — | — | — | — | |
| PEGNInput=PDGNN2022.01 | 86.7 | — | — | — | — | |
| PEGNInput=True Diagram2022.01 | 86.6 | — | — | — | — | |
| GNNSVDBackbone=GCN2023.10 | 86.49 | — | — | — | — | |
| HyboNetArchitecture Type=ANN-R2024.10 | 86.29 | — | — | — | — | |
| SSGCNumber of Layers=42025.01 | 85.95 | — | — | — | — | |
| Q-GCNArchitecture Type=ANN-R2024.10 | 85.94 | — | — | — | — | |
| CLNodeBackbone=GIN2023.10 | 85.93 | — | — | — | — | |
| GRADE-log2024.03 | 85.7 | — | — | — | — | |
| NBG2025.07 | 85.58 | — | — | — | — | |
| GAT-pprvariant=ppr2024.03 | 85.4 | — | — | — | — | |
| GRADE-GAT2024.03 | 85.4 | — | — | — | — | |
| APPNP2021.06 | 85.32 | — | 0.37 | — | — | |
| PTDNetBackbone=GraphSAGE2023.10 | 84.89 | — | — | — | — | |
| RGRL2023.12 | 84.83 | — | — | — | — | |
| GCNIINumber of Layers=82025.01 | 84.71 | — | — | — | — | |
| GRADE-Gaussian2024.03 | 84.6 | — | — | — | — | |
| APPNPNumber of Layers=42025.01 | 84.51 | — | — | — | — | |
| ACMP-GAT2024.03 | 84.4 | — | — | — | — | |
| SGCNumber of Layers=22025.01 | 84.13 | — | — | — | — | |
| GRAND++variant=++2024.03 | 84.1 | — | — | — | — | |
| SF2024.06 | 84.04 | — | — | — | — | |
| GRAND-lvariant=l2024.03 | 83.7 | — | — | — | — | |
| GCNArchitecture Type=ANN-E2024.10 | 83.55 | — | — | — | — |