Fraud Detection On Amazon Fraud
评估指标
AUC-ROC
Averaged Precision
评测结果
各个模型在此基准测试上的表现结果
模型名称 | 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 | - |
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