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Node Classification On Amz Photo

Metriken

Accuracy

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname	Accuracy	Paper Title	Repository
GLNN	92.11± 1.08%	Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillation
GraphSAGE	95.03%	Half-Hop: A graph upsampling approach for slowing down message passing
HH-GraphSAGE	94.55%	Half-Hop: A graph upsampling approach for slowing down message passing
NCSAGE	95.93 ± 0.36	Clarify Confused Nodes via Separated Learning
HH-GCN	94.52%	Half-Hop: A graph upsampling approach for slowing down message passing
CGT	95.73±0.84	Mitigating Degree Biases in Message Passing Mechanism by Utilizing Community Structures
Graph InfoClust (GIC)	90.4 ± 1.0	Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning
JK (Heat Diffusion)	92.93%	Diffusion Improves Graph Learning
Exphormer	95.35±0.22%	Exphormer: Sparse Transformers for Graphs
SIGN	91.72 ± 1.20	SIGN: Scalable Inception Graph Neural Networks
NCGCN	95.45 ± 0.45	Clarify Confused Nodes via Separated Learning
DAGNN (Ours)	92%	Towards Deeper Graph Neural Networks
GCN	93.59%	Half-Hop: A graph upsampling approach for slowing down message passing
CPF-ind-GAT	94.10%	Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework

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