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Node Classification On Coauthor Cs

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Model Name
Accuracy
Paper TitleRepository
HH-GCN94.71%Half-Hop: A graph upsampling approach for slowing down message passing
LinkDistMLP95.68%Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
LinkDist95.66%Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
NCSAGE96.48 ± 0.25Clarify Confused Nodes via Separated Learning
CoLinkDist95.80%Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
Exphormer94.93±0.46%Exphormer: Sparse Transformers for Graphs
GCN-LPA94.8 ± 0.4Unifying Graph Convolutional Neural Networks and Label Propagation
SNoRe88.7%SNoRe: Scalable Unsupervised Learning of Symbolic Node Representations-
3ference95.99%Inferring from References with Differences for Semi-Supervised Node Classification on Graphs
SIGN91.98 ± 0.50SIGN: Scalable Inception Graph Neural Networks
CoLinkDistMLP95.74%Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing Messages
Graph InfoClust (GIC)89.4 ± 0.4Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning
GNNMoE(SAGE-like P)95.68±0.24Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification
DAGNN (Ours)92.8%Towards Deeper Graph Neural Networks
NCGCN96.64 ± 0.29Clarify Confused Nodes via Separated Learning
GCN94.06%Half-Hop: A graph upsampling approach for slowing down message passing
GCN (PPR Diffusion)93.01%Diffusion Improves Graph Learning
GraphSAGE95.11%Half-Hop: A graph upsampling approach for slowing down message passing
GNNMoE(GAT-like P)95.72±0.23Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification
GraphMix (GCN)91.83 ± 0.51GraphMix: Improved Training of GNNs for Semi-Supervised Learning
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