HyperAI超神経
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HyperAI超神経

Node Classification On Cora 48 32 20 Fixed

評価指標

1:1 Accuracy

評価結果

このベンチマークにおける各モデルのパフォーマンス結果

モデル名
1:1 Accuracy
Paper TitleRepository
Gen-NSD87.30 ± 1.15Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
NLGAT 88.5 ± 1.8Non-Local Graph Neural Networks
ACMII-GCN88.01 ± 1.08Revisiting Heterophily For Graph Neural Networks
Geom-GCN85.35 ± 1.57Geom-GCN: Geometric Graph Convolutional Networks
ACMII-GCN+88.19 ± 1.17Revisiting Heterophily For Graph Neural Networks
GESN86.04 ± 1.01Addressing Heterophily in Node Classification with Graph Echo State Networks
ACM-SGC-186.9 ± 1.38Revisiting Heterophily For Graph Neural Networks
GGCN87.95 ± 1.05Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks
GPRGCN87.95 ± 1.18Adaptive Universal Generalized PageRank Graph Neural Network
ACM-SGC-287.69 ± 1.07Revisiting Heterophily For Graph Neural Networks
O(d)-NSD86.90 ± 1.13Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
GREAD-BS-GREAD: Graph Neural Reaction-Diffusion Networks
ACM-GCN++88.11 ± 0.96Revisiting Heterophily For Graph Neural Networks
NLGCN 88.1 ± 1.0Non-Local Graph Neural Networks
LINKX84.64 ± 1.13Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods
ACMII-GCN++88.25 ± 0.96Revisiting Heterophily For Graph Neural Networks
H2GCN87.87 ± 1.20Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs
GloGNN++88.33 ± 1.09Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
GloGNN88.31 ± 1.13Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
NLMLP 76.9 ± 1.8Non-Local Graph Neural Networks
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