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Node Classification On Non Homophilic 12

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
ScaleNet76.0±2.0Scale Invariance of Graph Neural Networks
Dir-GNN75.31±1.92Edge Directionality Improves Learning on Heterophilic Graphs
GESN73.56 ± 1.62Addressing Heterophily in Node Classification with Graph Echo State Networks
ACMII-GCN++67.4 ± 2.21Revisiting Heterophily For Graph Neural Networks
ACMII-GCN+67.07 ± 1.65Revisiting Heterophily For Graph Neural Networks
ACM-GCN++67.06 ± 1.66Revisiting Heterophily For Graph Neural Networks
ACM-GCN+66.98 ± 1.71Revisiting Heterophily For Graph Neural Networks
Deformable GCN62.56 ± 1.31Deformable Graph Convolutional Networks
LINKX61.81 ± 1.80Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods
NLGCN 59.0 ± 1.2Non-Local Graph Neural Networks
GloGNN++57.88 ± 1.76 Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
GloGNN57.54 ± 1.39 Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
NLGAT 56.8 ± 2.5Non-Local Graph Neural Networks
O(d)-NSD56.34 ± 1.32Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
ACM-GCN55.19 ± 1.49Revisiting Heterophily For Graph Neural Networks
GGCN55.17 ± 1.58Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks
Diag-NSD54.78 ± 1.81Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
Gen-NSD53.17 ± 1.31Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
ACMII-GCN51.8 ± 1.5Revisiting Heterophily For Graph Neural Networks
WRGAT48.85 ± 0.78Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns
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Node Classification On Non Homophilic 12 | SOTA | HyperAI超神経