Link Prediction On Pubmed
Métriques
AP
AUC
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | AP | AUC | Paper Title | Repository |
|---|---|---|---|---|
| Walkpooling | 98.7% | 98.7% | Neural Link Prediction with Walk Pooling | |
| Node Feature Agg + Similarity Metric | 94.2% | 94.5% | Rethinking Kernel Methods for Node Representation Learning on Graphs | |
| S-VGAE | 96.0% | 96.0% | Hyperspherical Variational Auto-Encoders | |
| Variational graph auto-encoders | - | - | Variational Graph Auto-Encoders | |
| MTGAE | - | - | Multi-Task Graph Autoencoders | |
| VGNAE | 97.6% | 97.6% | Variational Graph Normalized Auto-Encoders | |
| GNAE | 97.5% | 97.5% | Variational Graph Normalized Auto-Encoders | |
| Graph InfoClust (GIC) | 93.5% | 93.7% | Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning | |
| NBFNet | 98.2% | 98.3% | Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction | |
| NESS | 96.52% | 96.67% | NESS: Node Embeddings from Static SubGraphs | |
| ARGE | 97.1% | 96.8% | Adversarially Regularized Graph Autoencoder for Graph Embedding | |
| sGraphite-VAE | 96.3% | 94.8% | Mutual Information Maximization in Graph Neural Networks |
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