Graph Clustering On Cora
Métriques
ACC
ARI
F1
NMI
Precision
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | ACC | ARI | F1 | NMI | Precision |
|---|---|---|---|---|---|
| adversarially-regularized-graph-autoencoder | 64 | 35.2 | 61.9 | 0.449 | 64.6 |
| gscan-graph-stability-clustering-for | - | 49.6 | 71.7 | 52.4 | - |
| variational-graph-auto-encoders | 59.6 | - | - | - | - |
| rwr-gae-random-walk-regularization-for-graph | 66.9 | - | - | 48.1 | - |
| rethinking-graph-autoencoder-models-for | 76.7 | 57.9 | - | 57.3 | - |
| attributed-graph-clustering-via-adaptive | 68.92 | - | - | 53.68 | - |
| rethinking-graph-autoencoder-models-for | 73.7 | 54.1 | - | 56.0 | - |
| adversarially-regularized-graph-autoencoder | 63.8 | 37.4 | 62.7 | 45 | 62.4 |
| rwr-gae-random-walk-regularization-for-graph | 68.5 | - | - | 45.5 | - |