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Node Classification On Coauthor Physics

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Model Name
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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Node Classification On Coauthor Physics | SOTA | HyperAI