Node Classification On Non Homophilic 7

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1:1 Accuracy

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이 벤치마크에서 각 모델의 성능 결과

		Paper Title
ACMII-GCN++	86.49 ± 6.73	Revisiting Heterophily For Graph Neural Networks
Diag-NSD	86.49 ± 7.35	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
Deformable GCN	85.95±4.37	Deformable Graph Convolutional Networks
ACMII-GCN	85.95 ± 5.64	Revisiting Heterophily For Graph Neural Networks
GloGNN++	85.95 ± 5.10	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
ACM-GCN+	85.68 ± 4.84	Revisiting Heterophily For Graph Neural Networks
Gen-NSD	85.68 ± 6.51	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
GGCN	85.68 ± 6.63	Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks
ACM-GCN++	85.68 ± 5.8	Revisiting Heterophily For Graph Neural Networks
ACMII-GCN+	85.41 ± 5.3	Revisiting Heterophily For Graph Neural Networks
ACM-GCN	85.14 ± 6.07	Revisiting Heterophily For Graph Neural Networks
NLMLP	84.9 ± 5.7	Non-Local Graph Neural Networks
O(d) - NSD	84.86 ± 4.71	Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
GloGNN	83.51 ± 4.26	Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
H2GCN	82.70 ± 5.28	Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs
ACM-SGC-2	82.43 ± 5.44	Revisiting Heterophily For Graph Neural Networks
ACM-SGC-1	82.43 ± 5.44	Revisiting Heterophily For Graph Neural Networks
WRGAT	81.62 ±3.90	Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns
GESN	81.14 ± 6.00	Addressing Heterophily in Node Classification with Graph Echo State Networks
GPRGCN	78.11 ± 6.55	Adaptive Universal Generalized PageRank Graph Neural Network

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Node Classification On Non Homophilic 7

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