Node Classification On Wisconsin 60 20 20
المقاييس
1:1 Accuracy
النتائج
نتائج أداء النماذج المختلفة على هذا المعيار القياسي
جدول المقارنة
| اسم النموذج | 1:1 Accuracy |
|---|---|
| beyond-low-frequency-information-in-graph | 89.75 ± 6.37 |
| revisiting-heterophily-for-graph-neural | 96.62 ± 1.86 |
| predict-then-propagate-graph-neural-networks | 92.00 ± 3.59 |
| simple-and-deep-graph-convolutional-networks-1 | 89.12 ± 3.06 |
| revisiting-heterophily-for-graph-neural | 97.00 ± 2.63 |
| semi-supervised-classification-with-graph | 75.5 ± 2.92 |
| revisiting-heterophily-for-graph-neural | 96.5 ± 2.08 |
| break-the-ceiling-stronger-multi-scale-deep | 69.5 ± 5.01 |
| half-hop-a-graph-upsampling-approach-for | 79.8 ± 4.30 |
| geom-gcn-geometric-graph-convolutional-1 | 64.12 |
| revisiting-heterophily-for-graph-neural | 94.37 ± 2.81 |
| revisiting-heterophily-for-graph-neural | 96.63 ± 2.24 |
| revisiting-heterophily-for-graph-neural | 94.63 ± 2.96 |
| revisiting-heterophily-for-graph-neural | 96.75 ± 1.79 |
| revisiting-heterophily-for-graph-neural | 96.38 ± 2.59 |
| simplifying-graph-convolutional-networks | 74.75 ± 2.89 |
| half-hop-a-graph-upsampling-approach-for | 85.88 ± 3.99 |
| half-hop-a-graph-upsampling-approach-for | 83.53 ± 3.84 |
| generalizing-graph-neural-networks-beyond | 87.5 ± 1.77 |
| inductive-representation-learning-on-large | 64.85 ± 5.14 |
| simplifying-graph-convolutional-networks | 70.38 ± 2.85 |
| revisiting-heterophily-for-graph-neural | 97.13 ± 1.68 |
| revisiting-heterophily-for-graph-neural | 93.25 ± 2.92 |
| revisiting-heterophily-for-graph-neural | 62.50 ± 15.75 |
| simple-and-deep-graph-convolutional-networks-1 | 83.25 ± 2.69 |
| revisiting-heterophily-for-graph-neural | 69.50 ± 3.12 |
| revisiting-heterophily-for-graph-neural | 97.5 ± 1.25 |
| revisiting-heterophily-for-graph-neural | 95.75 ± 2.03 |
| graph-attention-networks | 71.01 ± 4.66 |
| break-the-ceiling-stronger-multi-scale-deep | 74.88 ± 3.42 |
| joint-adaptive-feature-smoothing-and-topology | 93.75 ± 2.37 |
| revisiting-heterophily-for-graph-neural | 96.62 ± 2.44 |
| revisiting-heterophily-for-graph-neural | 94.00 ± 2.61 |
| mixhop-higher-order-graph-convolution | 77.25 ± 7.80 |
| revisiting-heterophily-for-graph-neural | 93.87 ± 3.33 |