Node Classification On Film 60 20 20 Random
المقاييس
1:1 Accuracy
النتائج
نتائج أداء النماذج المختلفة على هذا المعيار القياسي
جدول المقارنة
| اسم النموذج | 1:1 Accuracy |
|---|---|
| geom-gcn-geometric-graph-convolutional-1 | 31.63 |
| graph-neural-networks-with-learnable-and | 42.39 ± 0.52 |
| graph-neural-networks-with-learnable-and | 43.05 ± 0.53 |
| revisiting-heterophily-for-graph-neural | 38.58 ± 0.25 |
| neighborhood-homophily-guided-graph-1 | 43.94 ± 1.14 |
| revisiting-heterophily-for-graph-neural | 41.79 ± 1.01 |
| simple-and-deep-graph-convolutional-networks-1 | 40.82 ± 1.79 |
| predict-then-propagate-graph-neural-networks | 38.86 ± 0.24 |
| bernnet-learning-arbitrary-graph-spectral | 41.79 ± 1.01 |
| beyond-low-frequency-information-in-graph | 31.59 ± 1.37 |
| revisiting-heterophily-for-graph-neural | 39.33 ± 1.25 |
| revisiting-heterophily-for-graph-neural | 41.5 ± 1.54 |
| revisiting-heterophily-for-graph-neural | 32.72 ± 2.62 |
| revisiting-heterophily-for-graph-neural | 41.37 ± 1.37 |
| transitivity-preserving-graph-representation | 36.84±0.62 |
| simplifying-graph-convolutional-networks | 28.81 ± 1.11 |
| revisiting-heterophily-for-graph-neural | 40.31 ± 1.6 |
| break-the-ceiling-stronger-multi-scale-deep | 36.00 ± 1.36 |
| semi-supervised-classification-with-graph | 35.51 ± 0.99 |
| revisiting-heterophily-for-graph-neural | 41.4 ± 1.23 |
| joint-adaptive-feature-smoothing-and-topology | 39.30 ± 0.27 |
| revisiting-heterophily-for-graph-neural | 41.27 ± 1.24 |
| revisiting-heterophily-for-graph-neural | 41.86 ± 1.48 |
| simple-and-deep-graph-convolutional-networks-1 | 41.54 ± 0.99 |
| revisiting-heterophily-for-graph-neural | 40.13 ± 1.21 |
| simplifying-graph-convolutional-networks | 25.26 ± 1.18 |
| inductive-representation-learning-on-large | 36.37 ± 0.21 |
| revisiting-heterophily-for-graph-neural | 41.27 ± 0.8 |
| revisiting-heterophily-for-graph-neural | 41.1 ± 0.75 |
| revisiting-heterophily-for-graph-neural | 35.41 ± 0.97 |
| revisiting-heterophily-for-graph-neural | 41.66 ± 1.42 |
| graph-attention-networks | 35.98 ± 0.23 |
| break-the-ceiling-stronger-multi-scale-deep | 35.97 ± 0.66 |
| gnndld-graph-neural-network-with-directional | 75.69±0.78 |
| beyond-low-frequency-information-in-graph | 38.85 ± 1.17 |
| mixhop-higher-order-graph-convolution | 33.13 ± 2.40 |
| revisiting-heterophily-for-graph-neural | 41.84 ± 1.15 |