Node Classification On Cornell 60 20 20

1:1 Accuracy

Results

Performance results of various models on this benchmark

		Paper Title
ACMII-GCN	95.9 ± 1.83	Revisiting Heterophily For Graph Neural Networks
ACMII-Snowball-2	95.25 ± 1.55	Revisiting Heterophily For Graph Neural Networks
ACM-Snowball-2	95.08 ± 3.11	Revisiting Heterophily For Graph Neural Networks
ACM-GCN+	94.92 ± 2.79	Revisiting Heterophily For Graph Neural Networks
ACM-GCN	94.75 ± 3.8	Revisiting Heterophily For Graph Neural Networks
ACM-Snowball-3	94.26 ± 2.57	Revisiting Heterophily For Graph Neural Networks
ACM-GCN++	93.93 ± 1.05	Revisiting Heterophily For Graph Neural Networks
ACMII-GCN+	93.93 ± 3.03	Revisiting Heterophily For Graph Neural Networks
ACM-SGC-2	93.77 ± 2.17	Revisiting Heterophily For Graph Neural Networks
ACM-SGC-1	93.77 ± 1.91	Revisiting Heterophily For Graph Neural Networks
ACMII-Snowball-3	93.61 ± 2.79	Revisiting Heterophily For Graph Neural Networks
ACM-GCNII*	93.44 ± 2.74	Revisiting Heterophily For Graph Neural Networks
ACMII-GCN++	92.62 ± 2.57	Revisiting Heterophily For Graph Neural Networks
ACM-GCNII	92.62 ± 3.13	Revisiting Heterophily For Graph Neural Networks
BernNet	92.13 ± 1.64	BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation
APPNP	91.80 ± 0.63	Predict then Propagate: Graph Neural Networks meet Personalized PageRank
GPRGNN	91.36 ± 0.70	Adaptive Universal Generalized PageRank Graph Neural Network
MLP-2	91.30 ± 0.70	Revisiting Heterophily For Graph Neural Networks
GCNII*	90.49 ± 4.45	Simple and Deep Graph Convolutional Networks
GCNII	89.18 ± 3.96	Simple and Deep Graph Convolutional Networks

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