Link Prediction On Cora
Metrics
AP
AUC
Results
Performance results of various models on this benchmark
Comparison Table
| Model Name | AP | AUC |
|---|---|---|
| rethinking-kernel-methods-for-node | 93.2% | 93.50% |
| neural-bellman-ford-networks-a-general-graph | 96.2% | 95.6% |
| binarized-attributed-network-embedding | 93.3% | 93.5% |
| hyperspherical-variational-auto-encoders | 94.1% | 94.1% |
| variational-graph-normalized-auto-encoders | 95.8% | 95.4% |
| neural-link-prediction-with-walk-pooling-1 | 96.0% | 95.9% |
| ness-learning-node-embeddings-from-static | 98.71% | 98.46% |
| variational-graph-normalized-auto-encoders | 95.7% | 95.6% |
| adversarially-regularized-graph-autoencoder | 93.2% | 92.4% |
| variational-graph-auto-encoders | - | - |
| graph-infoclust-leveraging-cluster-level-node | 93.5% | 93.7% |
| pppne-personalized-proximity-preserved | 93.9% | 92.5% |
| multi-task-graph-autoencoders | - | - |