Node Classification On Cora 60 20 20 Random

المقاييس

1:1 Accuracy

النتائج

نتائج أداء النماذج المختلفة على هذا المعيار القياسي

اسم النموذج	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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