Node Classification On Wisconsin
Metrics
Accuracy
Results
Performance results of various models on this benchmark
Model Name | Accuracy | Paper Title | Repository |
|---|---|---|---|
| HDP | 88.82 ± 3.40 | - | - |
| FAGCN | 79.61 ± 1.58 | - | - |
| Gen-NSD | 89.21 ± 3.84 | - | - |
| H2GCN-RARE (λ=1.0) | 90.00±2.97 | - | - |
| ACM-GCN+ | 88.43 ± 2.39 | - | - |
| LHS | 88.32±2.3 | - | - |
| H2GCN-1 | 84.31 ± 3.70 | - | - |
| GloGNN | 87.06±3.53 | - | - |
| GloGNN++ | 88.04±3.22 | - | - |
| M2M-GNN | 89.01 ± 4.1 | - | - |
| NLGAT | 56.9 ± 7.3 | - | - |
| ACM-SGC-2 | 86.47 ± 3.77 | - | - |
| GCNH | - | - | - |
| DJ-GNN | - | - | - |
| ACM-SGC-1 | 86.47 ± 3.77 | - | - |
| TE-GCNN | 87.45 ± 3.70 | - | - |
| Geom-GCN-I | 58.24 | - | - |
| FSGNN (3-hop) | 88.43±3.22 | - | - |
| H2GCN + UniGAP | 87.73 ± 4.8 | - | - |
| LINKX | 75.49 ± 5.72 | - | - |
0 of 63 row(s) selected.