Node Classification On Chameleon 60 20 20
المقاييس
1:1 Accuracy
النتائج
نتائج أداء النماذج المختلفة على هذا المعيار القياسي
جدول المقارنة
| اسم النموذج | 1:1 Accuracy |
|---|---|
| graph-attention-networks | 63.9 ± 0.46 |
| revisiting-heterophily-for-graph-neural | 61.66 ± 2.29 |
| semi-supervised-classification-with-graph | 64.18 ± 2.62 |
| revisiting-heterophily-for-graph-neural | 67.53 ± 2.83 |
| geom-gcn-geometric-graph-convolutional-1 | 60.9 |
| revisiting-heterophily-for-graph-neural | 64.68 ± 2.85 |
| revisiting-heterophily-for-graph-neural | 75.93 ± 1.71 |
| revisiting-heterophily-for-graph-neural | 58.73 ± 2.52 |
| revisiting-heterophily-for-graph-neural | 68.51 ± 1.7 |
| revisiting-heterophily-for-graph-neural | 67.83 ± 2.63 |
| inductive-representation-learning-on-large | 62.15 ± 0.42 |
| break-the-ceiling-stronger-multi-scale-deep | 65.49 ± 1.64 |
| revisiting-heterophily-for-graph-neural | 46.72 ± 0.46 |
| break-the-ceiling-stronger-multi-scale-deep | 64.99 ± 2.39 |
| simple-and-deep-graph-convolutional-networks-1 | 62.8 ± 2.87 |
| revisiting-heterophily-for-graph-neural | 75.51 ± 1.58 |
| mixhop-higher-order-graph-convolution | 36.28 ± 10.22 |
| half-hop-a-graph-upsampling-approach-for | 62.98 ± 3.35 |
| revisiting-heterophily-for-graph-neural | 68.4 ± 2.05 |
| revisiting-heterophily-for-graph-neural | 63.68 ± 1.62 |
| generalizing-graph-neural-networks-beyond | 52.30 ± 0.48 |
| predict-then-propagate-graph-neural-networks | 51.91 ± 0.56 |
| revisiting-heterophily-for-graph-neural | 68.38 ± 1.36 |
| revisiting-heterophily-for-graph-neural | 75.23 ± 1.72 |
| beyond-low-frequency-information-in-graph | 49.47 ± 2.84 |
| revisiting-heterophily-for-graph-neural | 76.08 ± 2.13 |
| gnndld-graph-neural-network-with-directional | 79.78±1.66 |
| simplifying-graph-convolutional-networks | 62.67 ± 2.41 |
| joint-adaptive-feature-smoothing-and-topology | 67.48 ± 0.40 |
| half-hop-a-graph-upsampling-approach-for | 60.24 ± 1.93 |
| simple-and-deep-graph-convolutional-networks-1 | 60.35 ± 2.7 |
| revisiting-heterophily-for-graph-neural | 68.14 ± 1.18 |
| half-hop-a-graph-upsampling-approach-for | 61.12 ± 1.83 |
| simplifying-graph-convolutional-networks | 64.86 ± 1.81 |
| revisiting-heterophily-for-graph-neural | 60.48 ± 1.55 |
| النموذج 36 | 78.9 |
| node-oriented-spectral-filtering-for-graph | 72.52±0.59 |
| bernnet-learning-arbitrary-graph-spectral | 68.29 ± 1.58 |