Link Prediction On Cora
المقاييس
AP
AUC
النتائج
نتائج أداء النماذج المختلفة على هذا المعيار القياسي
| Paper Title | |||
|---|---|---|---|
| NESS | 98.71% | 98.46% | NESS: Node Embeddings from Static SubGraphs |
| NBFNet | 96.2% | 95.6% | Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction |
| Walkpooling | 96.0% | 95.9% | Neural Link Prediction with Walk Pooling |
| VGNAE | 95.8% | 95.4% | Variational Graph Normalized Auto-Encoders |
| GNAE | 95.7% | 95.6% | Variational Graph Normalized Auto-Encoders |
| S-VGAE | 94.1% | 94.1% | Hyperspherical Variational Auto-Encoders |
| PPPNE | 93.9% | 92.5% | PPPNE: Personalized proximity preserved network embedding |
| sGraphite-VAE | 93.5% | 93.7% | Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning |
| Graph InfoClust (GIC) | 93.3% | 93.5% | Binarized Attributed Network Embedding |
| BANE | 93.2% | 93.50% | Rethinking Kernel Methods for Node Representation Learning on Graphs |
| ARGE | 93.2% | 92.4% | Adversarially Regularized Graph Autoencoder for Graph Embedding |
| Variational graph auto-encoders | - | - | Variational Graph Auto-Encoders |
| MTGAE | - | - | Multi-Task Graph Autoencoders |
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