Node Classification on ACTIVSg500
98.11AccuracyTRI-GNND
Evaluation results
| Method | Links | ||
|---|---|---|---|
| TRI-GNNDfiltration function=node degree2021.10 | 98.11 | 0.43 | |
| TRI-GNND2021.10 | 98.11 | — | |
| TRI-GNNAfiltration function=node attributes2021.10 | 97.85 | 0.56 | |
| LFGCN2021.10 | 97.61 | 0.47 | |
| PEGN-RC2021.10 | 97.2 | 0.5 | |
| GMNN2021.10 | 96.57 | 0.52 | |
| VPN2021.10 | 96.23 | 0.5 | |
| SPAGAN2021.10 | 96.19 | 0.4 | |
| APPNP2021.10 | 96.11 | 0.45 | |
| MixHop2021.10 | 95.94 | 0.4 | |
| RGCN2021.10 | 95.91 | 0.5 | |
| ChebNet2021.10 | 95.18 | 1 | |
| MotifNet2021.10 | 95.18 | 0.53 | |
| LGCNS2021.10 | 95.14 | 0.45 | |
| GAT2021.10 | 95 | 0.47 | |
| NodeNet2021.10 | 95 | — | |
| ARMA2021.10 | 94.33 | 0.47 | |
| GCN2021.10 | 90.33 | 0.68 |