HyperAI

Node Classification On Non Homophilic 4

Metriken

1:1 Accuracy

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Vergleichstabelle
Modellname1:1 Accuracy
revisiting-heterophily-for-graph-neural68.51 ± 1.7
revisiting-heterophily-for-graph-neural68.4 ± 2.05
mixhop-higher-order-graph-convolution36.28 ± 10.22
revisiting-heterophily-for-graph-neural63.68 ± 1.62
simplifying-graph-convolutional-networks62.67 ± 2.41
revisiting-heterophily-for-graph-neural67.53 ± 2.83
revisiting-heterophily-for-graph-neural60.48 ± 1.55
joint-adaptive-feature-smoothing-and-topology67.48 ± 0.40
inductive-representation-learning-on-large62.15 ± 0.42
revisiting-heterophily-for-graph-neural46.72 ± 0.46
revisiting-heterophily-for-graph-neural68.38 ± 1.36
revisiting-heterophily-for-graph-neural64.68 ± 2.85
revisiting-heterophily-for-graph-neural68.14 ± 1.18
revisiting-heterophily-for-graph-neural58.73 ± 2.52
beyond-low-frequency-information-in-graph49.47 ± 2.84
semi-supervised-classification-with-graph64.18 ± 2.62
simplifying-graph-convolutional-networks64.86 ± 1.81
bernnet-learning-arbitrary-graph-spectral68.29 ± 1.58
revisiting-heterophily-for-graph-neural75.23 ± 1.72
simple-and-deep-graph-convolutional-networks-162.8 ± 2.87
simple-and-deep-graph-convolutional-networks-160.35 ± 2.7
revisiting-heterophily-for-graph-neural76.08 ± 2.13
geom-gcn-geometric-graph-convolutional-160.9
generalizing-graph-neural-networks-beyond52.30 ± 0.48
revisiting-heterophily-for-graph-neural75.51 ± 1.58
revisiting-heterophily-for-graph-neural67.83 ± 2.63
revisiting-heterophily-for-graph-neural61.66 ± 2.29
graph-attention-networks63.9 ± 0.46
revisiting-heterophily-for-graph-neural75.93 ± 1.71
break-the-ceiling-stronger-multi-scale-deep65.49 ± 1.64
break-the-ceiling-stronger-multi-scale-deep64.99 ± 2.39
predict-then-propagate-graph-neural-networks51.91 ± 0.56