Node Classification On Cora 48 32 20 Fixed

المقاييس

1:1 Accuracy

النتائج

نتائج أداء النماذج المختلفة على هذا المعيار القياسي

اسم النموذج	1:1 Accuracy	Paper Title	Repository
Gen-NSD	87.30 ± 1.15	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs	-
NLGAT	88.5 ± 1.8	Non-Local Graph Neural Networks	-
ACMII-GCN	88.01 ± 1.08	Revisiting Heterophily For Graph Neural Networks	-
Geom-GCN	85.35 ± 1.57	Geom-GCN: Geometric Graph Convolutional Networks	-
ACMII-GCN+	88.19 ± 1.17	Revisiting Heterophily For Graph Neural Networks	-
GESN	86.04 ± 1.01	Addressing Heterophily in Node Classification with Graph Echo State Networks	-
ACM-SGC-1	86.9 ± 1.38	Revisiting Heterophily For Graph Neural Networks	-
GGCN	87.95 ± 1.05	Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks	-
GPRGCN	87.95 ± 1.18	Adaptive Universal Generalized PageRank Graph Neural Network	-
ACM-SGC-2	87.69 ± 1.07	Revisiting Heterophily For Graph Neural Networks	-
O(d)-NSD	86.90 ± 1.13	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs	-
GREAD-BS	-	GREAD: Graph Neural Reaction-Diffusion Networks	-
ACM-GCN++	88.11 ± 0.96	Revisiting Heterophily For Graph Neural Networks	-
NLGCN	88.1 ± 1.0	Non-Local Graph Neural Networks	-
LINKX	84.64 ± 1.13	Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods	-
ACMII-GCN++	88.25 ± 0.96	Revisiting Heterophily For Graph Neural Networks	-
H2GCN	87.87 ± 1.20	Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs	-
GloGNN++	88.33 ± 1.09	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily	-
GloGNN	88.31 ± 1.13	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily	-
NLMLP	76.9 ± 1.8	Non-Local Graph Neural Networks	-

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