Fraud Detection On Amazon Fraud
Métriques
AUC-ROC
Averaged Precision
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | AUC-ROC | Averaged Precision | Paper Title | Repository |
|---|---|---|---|---|
| PC-GNN | 95.86 | 85.49 | Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection | |
| LEX-GNN | 97.91 | 92.18 | LEX-GNN: Label-Exploring Graph Neural Network for Accurate Fraud Detection | |
| RLC-GNN | 97.48 | - | RLC-GNN: An Improved Deep Architecture for Spatial-Based Graph Neural Network with Application to Fraud Detection | - |
| RioGNN | 96.19 | - | Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks | |
| GTAN | 97.50 | 89.26 | Semi-supervised Credit Card Fraud Detection via Attribute-Driven Graph Representation | |
| CARE-GNN | 89.73 | 82.19 | Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters |
0 of 6 row(s) selected.