HyperAI
HyperAI
الرئيسية
المنصة
الوثائق
الأخبار
الأوراق البحثية
الدروس
مجموعات البيانات
الموسوعة
SOTA
نماذج LLM
لوحة الأداء GPU
الفعاليات
البحث
حول
شروط الخدمة
سياسة الخصوصية
العربية
HyperAI
HyperAI
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المنصة
الرئيسية
SOTA
تصنيف العقد
Node Classification On Amazon Computers 1
Node Classification On Amazon Computers 1
المقاييس
Accuracy
النتائج
نتائج أداء النماذج المختلفة على هذا المعيار القياسي
Columns
اسم النموذج
Accuracy
Paper Title
GAT
94.09±0.37
Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification
GCN
93.99±0.12
Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification
GraphSAGE
93.25±0.14
Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification
GNNMoE(GCN-like P)
92.17±0.50
Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification
GNNMoE(GAT-like P)
91.98±0.46
Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification
GNNMoE(SAGE-like P)
91.85±0.39
Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification
CGT
91.45±0.58
Mitigating Degree Biases in Message Passing Mechanism by Utilizing Community Structures
3ference
90.74%
Inferring from References with Differences for Semi-Supervised Node Classification on Graphs
LinkDist
89.49%
Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
LinkDistMLP
89.44%
Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
CoLinkDist
89.42%
Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
CoLinkDistMLP
88.85%
Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
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