Node Classification On Non Homophilic 8

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1:1 Accuracy

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이 벤치마크에서 각 모델의 성능 결과

		Paper Title
O(d)-NSD	89.41 ± 4.74	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
Gen-NSD	89.21 ± 3.84	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
Diag-NSD	88.63 ± 2.75	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
ACMII-GCN++	88.43 ± 3.66	Revisiting Heterophily For Graph Neural Networks
ACM-GCN+	88.43 ± 2.39	Revisiting Heterophily For Graph Neural Networks
ACM-GCN	88.43 ± 3.22	Revisiting Heterophily For Graph Neural Networks
ACM-GCN++	88.24 ± 3.16	Revisiting Heterophily For Graph Neural Networks
GloGNN++	88.04 ± 3.22	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
ACMII-GCN+	88.04 ± 3.66	Revisiting Heterophily For Graph Neural Networks
H2GCN	87.65 ± 4.98	Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs
ACMII-GCN	87.45 ± 3.74	Revisiting Heterophily For Graph Neural Networks
NLMLP	87.3 ± 4.3	Non-Local Graph Neural Networks
GloGNN	87.06 ± 3.53	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
WRGAT	86.98 ± 3.78	Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns
GGCN	86.86 ± 3.29	Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks
ACM-SGC-1	86.47 ± 3.77	Revisiting Heterophily For Graph Neural Networks
ACM-SGC-2	86.47 ± 3.77	Revisiting Heterophily For Graph Neural Networks
GESN	83.33 ± 3.81	Addressing Heterophily in Node Classification with Graph Echo State Networks
GPRGCN	82.55 ± 6.23	Adaptive Universal Generalized PageRank Graph Neural Network
GCNII	80.39 ± 3.40	Simple and Deep Graph Convolutional Networks

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Node Classification On Non Homophilic 8

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