Node Classification On Flickr
Métriques
Accuracy
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | Accuracy | Paper Title | Repository |
|---|---|---|---|
| GCN+GAugM (Zhao et al., 2021) | 0.682 | Data Augmentation for Graph Neural Networks | |
| GCN_cheby (Kipf and Welling, 2017) | 0.479 | Semi-Supervised Classification with Graph Convolutional Networks | |
| GraphSAGE (Hamilton et al., [2017a]) | 0.641 | Inductive Representation Learning on Large Graphs | |
| EnGCN (Duan et al., 2022) | 0.562 | A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking | |
| DEMO-Net(weight) | 0.656 ± 0.000 | DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification | |
| GCN (Kipf and Welling, 2017) | 0.546 | Semi-Supervised Classification with Graph Convolutional Networks | |
| GAT (Velickovic et al., 2018) | 0.359 | Graph Attention Networks | |
| Intersection (Li et al., 2018) | 0.557 | Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning |
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