Transductive Node Classification on Cora (transductive)
89.77AccuracyAutoscale
Evaluation results
| Method | Links | |||
|---|---|---|---|---|
| AutoscaleBase Model=Linear2020.10 | 89.77 | — | — | |
| AutoscaleBase Model=Plain Linear2020.10 | 89.54 | — | — | |
| FDiff-scaleBase Model=Linear2020.10 | 89.53 | — | — | |
| FDiff-scaleBase Model=Plain Linear2020.10 | 89.47 | — | — | |
| AutoscaleBase Model=MLP2020.10 | 88.55 | — | — | |
| SOTA2020.10 | 88.49 | — | — | |
| FDiff-scaleBase Model=MLP2020.10 | 88.18 | — | — | |
| OGCLearning Paradigm=supervised, #Layer=> 22023.09 | 86.9 | — | — | |
| GCNIILearning Paradigm=supervised, #Layer={64, 32, 16}2023.09 | 85.5 | — | — | |
| GRANDLearning Paradigm=supervised, #Layer={8, 2, 5}2023.09 | 85.4 | — | — | |
| ACMPLearning Paradigm=supervised, #Layer={8, 4, 32}2023.09 | 84.9 | — | — | |
| AIR-GCN2019.11 | 84.7 | — | — | |
| C&SLearning Paradigm=supervised, #Layer=32023.09 | 84.6 | — | — | |
| NDLSLearning Paradigm=supervised, #Layer=22023.09 | 84.6 | — | — | |
| AIR-GAT2019.11 | 84.5 | — | — | |
| DAGNN2019.11 | 84.4 | — | — | |
| GAMLPAttention Mechanism=JK attention2022.06 | 84.3 | — | — | |
| GNN-LF/HFLearning Paradigm=supervised, #Layer=22023.09 | 84 | — | — | |
| OAGSLearning Paradigm=supervised, #Layer=22023.09 | 83.9 | — | — | |
| GBP2022.06 | 83.9 | — | — | |
| GAMLPAttention Mechanism=Recursive attention2022.06 | 83.9 | — | — | |
| ChebNetIILearning Paradigm=supervised, #Layer=22023.09 | 83.7 | — | — | |
| GMNN2019.11 | 83.7 | — | — | |
| GGCMLearning Paradigm=unsupervised, #Layer=> 22023.09 | 83.6 | — | — | |
| AP-GCN2022.06 | 83.4 | — | — | |
| APPNPLearning Paradigm=supervised, #Layer=22023.09 | 83.3 | — | — | |
| GRACETraining Data=X, A2020.06 | 83.3 | — | — | |
| APPNP2022.06 | 83.3 | — | — | |
| N2NLearning Paradigm=unsupervised, #Layer=22023.09 | 83.1 | — | — | |
| S2GCLearning Paradigm=unsupervised, #Layer=162023.09 | 83 | — | — | |
| GAT2019.11 | 83 | — | — | |
| GAT2022.06 | 83 | — | — | |
| IncepLearning Paradigm=supervised, #Layer={64, 4, 4}2023.09 | 82.8 | — | — | |
| GCNTraining Data=X, A, Y2020.06 | 82.8 | — | — | |
| JKNetLearning Paradigm=supervised, #Layer={4, 16, 32}2023.09 | 82.7 | — | — | |
| G2CNLearning Paradigm=unsupervised, #Layer=102023.09 | 82.7 | — | — | |
| S2GC2022.06 | 82.7 | — | — | |
| DGITraining Data=X, A2020.06 | 82.6 | — | — | |
| DGILearning Paradigm=unsupervised, #Layer=22023.09 | 82.3 | — | — | |
| ResGCN2022.06 | 82.2 | — | — | |
| SIGN2022.06 | 82.1 | — | — | |
| GGCLearning Paradigm=unsupervised, #Layer=> 22023.09 | 81.8 | — | — | |
| GCN2022.06 | 81.8 | — | — | |
| JK-Net2022.06 | 81.8 | — | — | |
| GCNLearning Paradigm=supervised, #Layer=22023.09 | 81.5 | — | — | |
| GCN2019.11 | 81.5 | — | — | |
| BGRLLearning Paradigm=unsupervised, #Layer=22023.09 | 81.1 | — | — | |
| SGCLearning Paradigm=unsupervised, #Layer=22023.09 | 81 | — | — | |
| SGC2019.11 | 81 | — | — | |
| SGC2022.06 | 81 | — | — | |
| SGCTraining Data=X, A, Y2020.06 | 80.6 | — | — | |
| GLNNBackbone=MLP2021.10 | 79.39 | 20.44 | 0.61 | |
| Eigen-GCNLearning Paradigm=supervised, #Layer=22023.09 | 78.9 | — | — | |
| VGAETraining Data=X, A2020.06 | 78.9 | — | — | |
| SAGEBackbone=GraphSAGE2021.10 | 78.78 | — | — | |
| GIN-0epsilon=02019.11 | 78.3 | — | — | |
| GAETraining Data=X, A2020.06 | 76.9 | — | — | |
| GIN+εepsilon=learnable2019.11 | 76.6 | — | — | |
| DeepWalkTraining Data=A2020.06 | 75.7 | — | — | |
| Planetoid2019.11 | 75.7 | — | — | |
| node2vecTraining Data=A2020.06 | 74.8 | — | — | |
| DeepWalk + featuresTraining Data=X, A2020.06 | 73.1 | — | — | |
| VERSELearning Paradigm=unsupervised2023.09 | 72.5 | — | — | |
| LPLearning Paradigm=supervised2023.09 | 71.5 | — | — | |
| node2vecLearning Paradigm=unsupervised2023.09 | 71.5 | — | — | |
| GAELearning Paradigm=unsupervised, #Layer=42023.09 | 71.5 | — | — | |
| DGI Random-InitLearning Paradigm=unsupervised, #Layer=22023.09 | 69.3 | — | — | |
| DeepWalkLearning Paradigm=unsupervised2023.09 | 67.2 | — | — | |
| DeepWalk2019.11 | 67.2 | — | — | |
| Raw featuresTraining Data=X2020.06 | 64.8 | — | — | |
| ManiRegLearning Paradigm=supervised2023.09 | 59.5 | — | — | |
| MLPBackbone=MLP2021.10 | 58.95 | — | — |