HyperAI
الرئيسيةالأخبارأحدث الأوراق البحثيةالدروسمجموعات البياناتالموسوعةSOTAنماذج LLMلوحة الأداء GPUالفعاليات
البحث
حول
HyperAI

Node Classification On Coauthor Physics

المقاييس

Accuracy

النتائج

نتائج أداء النماذج المختلفة على هذا المعيار القياسي

اسم النموذج
Accuracy
Paper TitleRepository
GNNMoE(GCN-like P)97.03±0.13Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification
LinkDist96.87%Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
CoLinkDist97.05%Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
Exphormer96.89±0.09%Exphormer: Sparse Transformers for Graphs
GNNMoE(SAGE-like P)96.81±0.22Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification
DAGNN (Ours)94Towards Deeper Graph Neural Networks
GCN97.46 ± 0.10Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification
3ference97.22%Inferring from References with Differences for Semi-Supervised Node Classification on Graphs
GraphMix (GCN)94.49 ± 0.84GraphMix: Improved Training of GNNs for Semi-Supervised Learning
CoLinkDistMLP96.87%Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
LinkDistMLP96.91%Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
GNNMoE(GAT-like P)97.05±0.19Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification
NCSAGE98.69 ± 0.26Clarify Confused Nodes via Separated Learning
NCGCN98.63 ± 0.24Clarify Confused Nodes via Separated Learning
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