Node Classification On Non Homophilic 4

1:1 Accuracy

평가 결과

이 벤치마크에서 각 모델의 성능 결과

		Paper Title
ACM-GCN+	76.08 ± 2.13	Revisiting Heterophily For Graph Neural Networks
ACMII-GCN++	75.93 ± 1.71	Revisiting Heterophily For Graph Neural Networks
ACMII-GCN+	75.51 ± 1.58	Revisiting Heterophily For Graph Neural Networks
ACM-GCN++	75.23 ± 1.72	Revisiting Heterophily For Graph Neural Networks
ACM-Snowball-2	68.51 ± 1.7	Revisiting Heterophily For Graph Neural Networks
ACM-Snowball-3	68.4 ± 2.05	Revisiting Heterophily For Graph Neural Networks
ACMII-GCN	68.38 ± 1.36	Revisiting Heterophily For Graph Neural Networks
BernNet	68.29 ± 1.58	BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation
GAT+JK	68.14 ± 1.18	Revisiting Heterophily For Graph Neural Networks
ACMII-Snowball-2	67.83 ± 2.63	Revisiting Heterophily For Graph Neural Networks
ACMII-Snowball-3	67.53 ± 2.83	Revisiting Heterophily For Graph Neural Networks
GPRGNN	67.48 ± 0.40	Adaptive Universal Generalized PageRank Graph Neural Network
Snowball-3	65.49 ± 1.64	Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
Snowball-2	64.99 ± 2.39	Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
SGC-1	64.86 ± 1.81	Simplifying Graph Convolutional Networks
GCN+JK	64.68 ± 2.85	Revisiting Heterophily For Graph Neural Networks
GCN	64.18 ± 2.62	Semi-Supervised Classification with Graph Convolutional Networks
GAT	63.9 ± 0.46	Graph Attention Networks
ACM-SGC-1	63.68 ± 1.62	Revisiting Heterophily For Graph Neural Networks
GCNII*	62.8 ± 2.87	Simple and Deep Graph Convolutional Networks

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