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Node Classification On Pubmed 003

Métriques

Accuracy

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle
Accuracy
Paper TitleRepository
VCHN71.8%View-Consistent Heterogeneous Network on Graphs With Few Labeled Nodes
DCNN60.9%Diffusion-Convolutional Neural Networks-
Truncated Krylov71.11%Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks-
GGNN55.8%Gated Graph Sequence Neural Networks-
Snowball (tanh)62.61%Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks-
GAT50.9%Graph Attention Networks-
ChebyNet45.3%Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering-
MT-GCN65.5%Mutual Teaching for Graph Convolutional Networks-
Snowball (linear + tanh)61.94%Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks-
Snowball (linear)68.12%Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks-
AdaLanczosNet61%LanczosNet: Multi-Scale Deep Graph Convolutional Networks-
GCN-FP56.2%Convolutional Networks on Graphs for Learning Molecular Fingerprints-
LanczosNet60.4 ± 8.6LanczosNet: Multi-Scale Deep Graph Convolutional Networks-
GraphSAGE45.4%Inductive Representation Learning on Large Graphs-
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Node Classification On Pubmed 003 | SOTA | HyperAI