Node Classification On Aifb
Métriques
Accuracy
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | Accuracy | Paper Title | Repository |
|---|---|---|---|
| RR-GCN-PPV | 86.11 | R-GCN: The R Could Stand for Random | |
| RDF2Vec+SVM | 88.88 | RDF2Vec: RDF Graph Embeddings and Their Applications | |
| BoP | 92.22 | From Primes to Paths: Enabling Fast Multi-Relational Graph Analysis | |
| R-GCN | 95.83 | Modeling Relational Data with Graph Convolutional Networks | |
| RR-GCN-PPV-CUT | 95.83 | R-GCN: The R Could Stand for Random | |
| Path Tree | 89.44 | Inducing a Decision Tree with Discriminative Paths to Classify Entities in a Knowledge Graph | |
| SCENE | 95.83 | SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks |
0 of 7 row(s) selected.