Node Classification On Non Homophilic 10

1:1 Accuracy

评测结果

各个模型在此基准测试上的表现结果

比较表格

模型名称	1:1 Accuracy
non-local-graph-neural-networks	29.5 ± 1.3
revisiting-heterophily-for-graph-neural	35.49 ± 1.06
mixhop-higher-order-graph-convolution	32.22 ± 2.34
revisiting-heterophily-for-graph-neural	37.31 ± 1.09
revisiting-heterophily-for-graph-neural	36.63 ± 0.84
revisiting-heterophily-for-graph-neural	36.31 ± 1.2
non-local-graph-neural-networks	37.9 ± 1.3
generalizing-graph-neural-networks-beyond	35.70 ± 1.00
neural-sheaf-diffusion-a-topological	37.79 ± 1.01
two-sides-of-the-same-coin-heterophily-and	37.54 ± 1.56
geom-gcn-geometric-graph-convolutional-1	31.59 ± 1.15
revisiting-heterophily-for-graph-neural	36.14 ± 1.44
two-sides-of-the-same-coin-heterophily-and	35.16 ± 0.9
finding-global-homophily-in-graph-neural	37.35 ± 1.30
neural-sheaf-diffusion-a-topological	37.80 ± 1.22
deformable-graph-convolutional-networks	37.07±0.79
simple-and-deep-graph-convolutional-networks-1	37.44 ± 1.30
finding-global-homophily-in-graph-neural	37.70 ± 1.40
large-scale-learning-on-non-homophilous	36.10 ± 1.55
revisiting-heterophily-for-graph-neural	36.26 ± 1.34
revisiting-heterophily-for-graph-neural	36.04 ± 0.83
neural-sheaf-diffusion-a-topological	37.81 ± 1.15
non-local-graph-neural-networks	31.6 ± 1.0
breaking-the-limit-of-graph-neural-networks	36.53 ± 0.77
revisiting-heterophily-for-graph-neural	37.09 ± 1.32
beyond-low-frequency-information-in-graph	34.82 ± 1.35