Node Classification On Coauthor Cs

评估指标

Accuracy

评测结果

各个模型在此基准测试上的表现结果

模型名称	Accuracy	Paper Title	Repository
HH-GCN	94.71%	Half-Hop: A graph upsampling approach for slowing down message passing	-
LinkDistMLP	95.68%	Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages	-
LinkDist	95.66%	Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages	-
NCSAGE	96.48 ± 0.25	Clarify Confused Nodes via Separated Learning	-
CoLinkDist	95.80%	Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages	-
Exphormer	94.93±0.46%	Exphormer: Sparse Transformers for Graphs	-
GCN-LPA	94.8 ± 0.4	Unifying Graph Convolutional Neural Networks and Label Propagation	-
SNoRe	88.7%	SNoRe: Scalable Unsupervised Learning of Symbolic Node Representations	-
3ference	95.99%	Inferring from References with Differences for Semi-Supervised Node Classification on Graphs
SIGN	91.98 ± 0.50	SIGN: Scalable Inception Graph Neural Networks	-
CoLinkDistMLP	95.74%	Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages	-
Graph InfoClust (GIC)	89.4 ± 0.4	Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning	-
GNNMoE(SAGE-like P)	95.68±0.24	Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification	-
DAGNN (Ours)	92.8%	Towards Deeper Graph Neural Networks	-
NCGCN	96.64 ± 0.29	Clarify Confused Nodes via Separated Learning	-
GCN	94.06%	Half-Hop: A graph upsampling approach for slowing down message passing	-
GCN (PPR Diffusion)	93.01%	Diffusion Improves Graph Learning	-
GraphSAGE	95.11%	Half-Hop: A graph upsampling approach for slowing down message passing	-
GNNMoE(GAT-like P)	95.72±0.23	Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification	-
GraphMix (GCN)	91.83 ± 0.51	GraphMix: Improved Training of GNNs for Semi-Supervised Learning	-

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