Node Classification On Wisconsin 60 20 20

1:1 Accuracy

Results

Performance results of various models on this benchmark

		Paper Title
ACM-GCN++	97.5 ± 1.25	Revisiting Heterophily For Graph Neural Networks
ACMII-GCN++	97.13 ± 1.68	Revisiting Heterophily For Graph Neural Networks
ACMII-Snowball-3	97.00 ± 2.63	Revisiting Heterophily For Graph Neural Networks
ACMII-GCN+	96.75 ± 1.79	Revisiting Heterophily For Graph Neural Networks
ACMII-Snowball-2	96.63 ± 2.24	Revisiting Heterophily For Graph Neural Networks
ACM-Snowball-3	96.62 ± 1.86	Revisiting Heterophily For Graph Neural Networks
ACMII-GCN	96.62 ± 2.44	Revisiting Heterophily For Graph Neural Networks
ACM-GCN+	96.5 ± 2.08	Revisiting Heterophily For Graph Neural Networks
ACM-Snowball-2	96.38 ± 2.59	Revisiting Heterophily For Graph Neural Networks
ACM-GCN	95.75 ± 2.03	Revisiting Heterophily For Graph Neural Networks
ACM-GCNII	94.63 ± 2.96	Revisiting Heterophily For Graph Neural Networks
ACM-GCNII*	94.37 ± 2.81	Revisiting Heterophily For Graph Neural Networks
ACM-SGC-2	94.00 ± 2.61	Revisiting Heterophily For Graph Neural Networks
MLP-2	93.87 ± 3.33	Revisiting Heterophily For Graph Neural Networks
GPRGNN	93.75 ± 2.37	Adaptive Universal Generalized PageRank Graph Neural Network
ACM-SGC-1	93.25 ± 2.92	Revisiting Heterophily For Graph Neural Networks
APPNP	92.00 ± 3.59	Predict then Propagate: Graph Neural Networks meet Personalized PageRank
FAGCN	89.75 ± 6.37	Beyond Low-frequency Information in Graph Convolutional Networks
GCNII*	89.12 ± 3.06	Simple and Deep Graph Convolutional Networks
H2GCN	87.5 ± 1.77	Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs

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