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Node Classification On Non Homophilic 8

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

Paper Title
O(d)-NSD89.41 ± 4.74Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
Gen-NSD89.21 ± 3.84Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
Diag-NSD88.63 ± 2.75Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
ACMII-GCN++88.43 ± 3.66Revisiting Heterophily For Graph Neural Networks
ACM-GCN+88.43 ± 2.39Revisiting Heterophily For Graph Neural Networks
ACM-GCN88.43 ± 3.22Revisiting Heterophily For Graph Neural Networks
ACM-GCN++88.24 ± 3.16Revisiting Heterophily For Graph Neural Networks
GloGNN++ 88.04 ± 3.22 Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
ACMII-GCN+88.04 ± 3.66Revisiting Heterophily For Graph Neural Networks
H2GCN87.65 ± 4.98Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs
ACMII-GCN87.45 ± 3.74Revisiting Heterophily For Graph Neural Networks
NLMLP 87.3 ± 4.3 Non-Local Graph Neural Networks
GloGNN87.06 ± 3.53 Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
WRGAT86.98 ± 3.78 Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns
GGCN86.86 ± 3.29 Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks
ACM-SGC-186.47 ± 3.77Revisiting Heterophily For Graph Neural Networks
ACM-SGC-286.47 ± 3.77Revisiting Heterophily For Graph Neural Networks
GESN83.33 ± 3.81Addressing Heterophily in Node Classification with Graph Echo State Networks
GPRGCN82.55 ± 6.23Adaptive Universal Generalized PageRank Graph Neural Network
GCNII80.39 ± 3.40Simple and Deep Graph Convolutional Networks
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Node Classification On Non Homophilic 8 | SOTA | HyperAI超神経