Link Prediction On Citeseer
Metriken
AP
AUC
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Vergleichstabelle
| Modellname | AP | AUC |
|---|---|---|
| rethinking-kernel-methods-for-node | 91.8% | 90.9% |
| multi-task-graph-autoencoders | - | - |
| variational-graph-normalized-auto-encoders | 97.1 | 97 |
| neural-bellman-ford-networks-a-general-graph | 93.6% | 92.3% |
| binarized-attributed-network-embedding | - | 95.59% |
| graph-infoclust-leveraging-cluster-level-node | 96.8 | 97 |
| hyperspherical-variational-auto-encoders | 95.2 | 94.7 |
| adversarially-regularized-graph-autoencoder | 93 | 91.9 |
| graphite-iterative-generative-modeling-of | 95.4% | 94.1% |
| neural-link-prediction-with-walk-pooling-1 | 96.04 | 95.94 |
| variational-graph-auto-encoders | - | - |
| ness-learning-node-embeddings-from-static | 99.5 | 99.43 |
| variational-graph-normalized-auto-encoders | 97 | 96.5 |