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المنصة
الرئيسية
SOTA
تصنيف العقد في الرسوم البيانية غير المتجانسة (متجانسة الخصائص المختلفة)
Node Classification On Non Homophilic 7
Node Classification On Non Homophilic 7
المقاييس
1:1 Accuracy
النتائج
نتائج أداء النماذج المختلفة على هذا المعيار القياسي
Columns
اسم النموذج
1:1 Accuracy
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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