Node Classification On Non Homophilic 7
Métriques
1:1 Accuracy
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | 1:1 Accuracy |
|---|---|
| revisiting-heterophily-for-graph-neural | 85.68 ± 4.84 |
| mixhop-higher-order-graph-convolution | 73.51 ± 6.34 |
| non-local-graph-neural-networks | 84.9 ± 5.7 |
| revisiting-heterophily-for-graph-neural | 82.43 ± 5.44 |
| non-local-graph-neural-networks | 57.6 ± 5.5 |
| deformable-graph-convolutional-networks | 85.95±4.37 |
| large-scale-learning-on-non-homophilous | 77.84 ± 5.81 |
| revisiting-heterophily-for-graph-neural | 86.49 ± 6.73 |
| breaking-the-limit-of-graph-neural-networks | 81.62 ±3.90 |
| beyond-low-frequency-information-in-graph | 76.76 ± 5.87 |
| non-local-graph-neural-networks | 54.7 ± 7.6 |
| generalizing-graph-neural-networks-beyond | 82.70 ± 5.28 |
| simple-and-deep-graph-convolutional-networks-1 | 77.86 ± 3.79 |
| geom-gcn-geometric-graph-convolutional-1 | 60.54 ± 3.67 |
| neural-sheaf-diffusion-a-topological | 85.68 ± 6.51 |
| revisiting-heterophily-for-graph-neural | 82.43 ± 5.44 |
| revisiting-heterophily-for-graph-neural | 85.95 ± 5.64 |
| two-sides-of-the-same-coin-heterophily-and | 85.68 ± 6.63 |
| finding-global-homophily-in-graph-neural | 85.95 ± 5.10 |
| revisiting-heterophily-for-graph-neural | 85.41 ± 5.3 |
| revisiting-heterophily-for-graph-neural | 85.14 ± 6.07 |
| revisiting-heterophily-for-graph-neural | 85.68 ± 5.8 |
| neural-sheaf-diffusion-a-topological | 84.86 ± 4.71 |
| neural-sheaf-diffusion-a-topological | 86.49 ± 7.35 |
| addressing-heterophily-in-node-classification | 81.14 ± 6.00 |
| finding-global-homophily-in-graph-neural | 83.51 ± 4.26 |
| joint-adaptive-feature-smoothing-and-topology | 78.11 ± 6.55 |