Node Classification On Cora 60 20 20 Random

평가 지표

1:1 Accuracy

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름	1:1 Accuracy	Paper Title	Repository
GPRGNN	79.51 ± 0.36	Adaptive Universal Generalized PageRank Graph Neural Network
GCNII	88.98 ± 1.33	Simple and Deep Graph Convolutional Networks
GNNDLD	92.99 ±0.9	GNNDLD: Graph Neural Network with Directional Label Distribution	-
Geom-GCN*	85.27	Geom-GCN: Geometric Graph Convolutional Networks
ACM-GCN+	89.75 ± 1.16	Revisiting Heterophily For Graph Neural Networks
ACM-GCNII*	89.00 ± 1.35	Revisiting Heterophily For Graph Neural Networks
ACM-SGC-2	87.64 ± 0.99	Revisiting Heterophily For Graph Neural Networks
BernNet	88.52 ± 0.95	BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation
SGC-1	85.12 ± 1.64	Simplifying Graph Convolutional Networks
H2GCN	87.52 ± 0.61	Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs
ACM-GCN++	89.33 ± 0.81	Revisiting Heterophily For Graph Neural Networks
MLP-2	76.44 ± 0.30	Revisiting Heterophily For Graph Neural Networks
ACMII-GCN++	89.47 ± 1.08	Revisiting Heterophily For Graph Neural Networks
ACMII-Snowball-3	89.36 ± 1.26	Revisiting Heterophily For Graph Neural Networks
GCN+JK	86.90 ± 1.51	007: Democratically Finding The Cause of Packet Drops
ACMII-GCN	89.00 ± 0.72	Revisiting Heterophily For Graph Neural Networks
GCNII*	88.93 ± 1.37	Simple and Deep Graph Convolutional Networks
Snowball-2	88.64 ± 1.15	Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks
ACM-SGC-1	86.63 ± 1.13	Revisiting Heterophily For Graph Neural Networks
Snowball-3	89.33 ± 1.3	Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks

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