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Node Classification On Amz Photo

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Model Name
Accuracy
Paper TitleRepository
GLNN92.11± 1.08%Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillation-
GraphSAGE95.03%Half-Hop: A graph upsampling approach for slowing down message passing-
HH-GraphSAGE94.55%Half-Hop: A graph upsampling approach for slowing down message passing-
NCSAGE95.93 ± 0.36Clarify Confused Nodes via Separated Learning-
HH-GCN94.52%Half-Hop: A graph upsampling approach for slowing down message passing-
CGT95.73±0.84Mitigating Degree Biases in Message Passing Mechanism by Utilizing Community Structures-
Graph InfoClust (GIC)90.4 ± 1.0Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning-
JK (Heat Diffusion)92.93%Diffusion Improves Graph Learning-
Exphormer95.35±0.22%Exphormer: Sparse Transformers for Graphs-
SIGN91.72 ± 1.20SIGN: Scalable Inception Graph Neural Networks-
NCGCN95.45 ± 0.45Clarify Confused Nodes via Separated Learning-
DAGNN (Ours)92%Towards Deeper Graph Neural Networks-
GCN93.59%Half-Hop: A graph upsampling approach for slowing down message passing-
CPF-ind-GAT94.10%Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework-
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Node Classification On Amz Photo | SOTA | HyperAI