Link Prediction On Citeseer
평가 지표
AP
AUC
평가 결과
이 벤치마크에서 각 모델의 성능 결과
모델 이름 | AP | AUC | Paper Title | Repository |
|---|---|---|---|---|
| Node Feature Agg + Similarity Metric | 91.8% | 90.9% | Rethinking Kernel Methods for Node Representation Learning on Graphs | - |
| MTGAE | - | - | Multi-Task Graph Autoencoders | - |
| VGNAE | 97.1 | 97 | Variational Graph Normalized Auto-Encoders | - |
| NBFNet | 93.6% | 92.3% | Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction | - |
| BANE | - | 95.59% | Binarized Attributed Network Embedding | |
| Graph InfoClust (GIC) | 96.8 | 97 | Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning | - |
| S-VGAE | 95.2 | 94.7 | Hyperspherical Variational Auto-Encoders | - |
| ARGE | 93 | 91.9 | Adversarially Regularized Graph Autoencoder for Graph Embedding | - |
| sGraphite-VAE | 95.4% | 94.1% | Graphite: Iterative Generative Modeling of Graphs | - |
| Walkpooling | 96.04 | 95.94 | Neural Link Prediction with Walk Pooling | - |
| Variational graph auto-encoders | - | - | Variational Graph Auto-Encoders | - |
| NESS | 99.5 | 99.43 | NESS: Node Embeddings from Static SubGraphs | - |
| GNAE | 97 | 96.5 | Variational Graph Normalized Auto-Encoders | - |
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