Node Classification on Citeseer (60 20 20 random split)
81.39AccuracyGCN
Evaluation results
| Method | Links | ||
|---|---|---|---|
| GCN2022.05 | 81.39 | — | |
| ChebNetIISplits=20 random 60%/20%/20% train/validation/test2022.10 | 80.53 | — | |
| tGNN2022.05 | 80.51 | — | |
| APPNPSplits=20 random 60%/20%/20% train/validation/test2022.10 | 80.47 | — | |
| ClenshawGCN2022.10 | 80.34 | — | |
| ClenshawGCNSplits=20 random 60%/20%/20% train/validation/test2022.10 | 80.34 | — | |
| GPRGNNSplits=20 random 60%/20%/20% train/validation/test2022.10 | 80.13 | — | |
| BernNetSplits=20 random 60%/20%/20% train/validation/test2022.10 | 80.08 | — | |
| ARMASplits=20 random 60%/20%/20% train/validation/test2022.10 | 80.04 | — | |
| H2GCN2022.05 | 79.97 | — | |
| GCNII2022.10 | 79.97 | — | |
| H2GCN2022.10 | 79.97 | — | |
| GCN2022.10 | 79.85 | — | |
| ChebNetSplits=20 random 60%/20%/20% train/validation/test2022.10 | 79.33 | — | |
| MLP2022.10 | 76.52 | — | |
| GraphSAGE2022.05 | 76.24 | — | |
| GCN+JK2022.10 | 73.77 | — | |
| APPNP2022.05 | 68.59 | — | |
| GPRGNN2022.05 | 67.63 | — | |
| GAT2022.05 | 67.2 | — | |
| MixHop2022.05 | 49.52 | — | |
| MixHop2022.10 | 49.52 | — | |
| Best C&S modelParameter Δ=-89.68%, Time=0.48 s2020.10 | — | -0.69 |