Node Classification on ACTIVSg200
86.18AccuracyTRI-GNND
Evaluation results
| Method | Links | ||
|---|---|---|---|
| TRI-GNNDfiltration function=node degree2021.10 | 86.18 | 0.2 | |
| TRI-GNND2021.10 | 86.18 | — | |
| TRI-GNNAfiltration function=node attributes2021.10 | 84.93 | 1.51 | |
| GMNN2021.10 | 84.13 | 1.89 | |
| LGCNS2021.10 | 83.78 | 1.5 | |
| GAT2021.10 | 83.56 | 2.58 | |
| RGCN2021.10 | 83.35 | 3.12 | |
| PEGN-RC2021.10 | 83.3 | 1.1 | |
| GCN2021.10 | 82.96 | 1.66 | |
| SPAGAN2021.10 | 81.83 | 2.09 | |
| APPNP2021.10 | 81.21 | 1.85 | |
| LFGCN2021.10 | 81.11 | 2.1 | |
| ChebNet2021.10 | 80.63 | 4.2 | |
| MixHop2021.10 | 80.53 | 1.96 | |
| NodeNet2021.10 | 80.15 | — | |
| ARMA2021.10 | 80.07 | 3.3 | |
| VPN2021.10 | 79.89 | 1.82 | |
| MotifNet2021.10 | 73.2 | 1.61 |