Node Classification On Facebook
Métriques
Accuracy
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | Accuracy | Paper Title | Repository |
|---|---|---|---|
| GNNMoE(SAGE-like P) | 94.63±0.36 | Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification | |
| Intersection (Li et al., 2018) | 59.8 | Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning | |
| GCN_cheby (Kipf and Welling, 2017) | 64.6 | Semi-Supervised Classification with Graph Convolutional Networks | |
| GraphSAGE (Hamilton et al., [2017a]) | 38.9 | Inductive Representation Learning on Large Graphs | |
| GNNMoE(GCN-like P) | 95.53±0.35 | Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification | |
| GCN (Kipf and Welling, 2017) | 57.5 | Semi-Supervised Classification with Graph Convolutional Networks | |
| DEMO-Net(weight) | 91.9 | DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification | |
| GNNMoE(GAT-like P) | 95.21±0.25 | Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node Classification |
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