Node Classification On Genius
المقاييس
1:1 Accuracy
النتائج
نتائج أداء النماذج المختلفة على هذا المعيار القياسي
جدول المقارنة
| اسم النموذج | 1:1 Accuracy |
|---|---|
| addressing-heterophily-in-node-classification | 91.72 ± 0.08 |
| large-scale-learning-on-non-homophilous | - |
| revisiting-heterophily-for-graph-neural | - |
| revisiting-heterophily-for-graph-neural | - |
| revisiting-heterophily-for-graph-neural | - |
| joint-adaptive-feature-smoothing-and-topology | - |
| finding-global-homophily-in-graph-neural | - |
| revisiting-heterophily-for-graph-neural | - |
| simple-and-deep-graph-convolutional-networks-1 | - |
| new-benchmarks-for-learning-on-non | - |
| mixhop-higher-order-graph-convolution | - |
| feature-selection-key-to-enhance-node | - |
| finding-global-homophily-in-graph-neural | - |
| new-benchmarks-for-learning-on-non | - |
| new-benchmarks-for-learning-on-non | - |
| predict-then-propagate-graph-neural-networks | - |
| simplifying-graph-convolutional-networks | - |
| finding-global-homophily-in-graph-neural | - |
| combining-label-propagation-and-simple-models-1 | - |
| simplifying-graph-convolutional-networks | - |
| gradient-gating-for-deep-multi-rate-learning | - |
| new-benchmarks-for-learning-on-non | - |
| combining-label-propagation-and-simple-models-1 | - |
| finding-global-homophily-in-graph-neural | - |
| semi-supervised-classification-with-graph | - |
| graph-attention-networks | - |