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المنصة

Node Classification On Non Homophilic 9

المقاييس

1:1 Accuracy

النتائج

نتائج أداء النماذج المختلفة على هذا المعيار القياسي

Paper Title
ACM-GCN++88.38 ± 3.43Revisiting Heterophily For Graph Neural Networks
ACM-GCN+88.38 ± 3.64Revisiting Heterophily For Graph Neural Networks
ACMII-GCN++88.38 ± 3.43Revisiting Heterophily For Graph Neural Networks
ACMII-GCN+88.11 ± 3.24Revisiting Heterophily For Graph Neural Networks
ACM-GCN87.84 ± 4.4Revisiting Heterophily For Graph Neural Networks
ACMII-GCN86.76 ± 4.75Revisiting Heterophily For Graph Neural Networks
O(d)-NSD85.95 ± 5.51Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
Diag-NSD85.67 ± 6.95Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
NLMLP 85.4 ± 3.8Non-Local Graph Neural Networks
H2GCN84.86 ± 7.23Beyond Low-frequency Information in Graph Convolutional Networks
GGCN84.86 ± 4.55Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks
GloGNN84.32 ± 4.15 Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
GESN84.31 ± 4.44Addressing Heterophily in Node Classification with Graph Echo State Networks
GloGNN++84.05 ± 4.90Finding Global Homophily in Graph Neural Networks When Meeting Heterophily
WRGAT83.62 ± 5.50 Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing Patterns
Gen-NSD82.97 ± 5.13 Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs
ACM-SGC-281.89 ± 4.53Revisiting Heterophily For Graph Neural Networks
ACM-SGC-181.89 ± 4.53Revisiting Heterophily For Graph Neural Networks
GPRGCN81.35 ± 5.32Adaptive Universal Generalized PageRank Graph Neural Network
MixHop77.84 ± 7.73 MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing
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Node Classification On Non Homophilic 9 | SOTA | HyperAI