التفاصيل البيبلوغرافية
| العنوان: |
GNN-EADD: Graph Neural Network-Based E-Commerce Anomaly Detection via Dual-Stage Learning |
| المؤلفون: |
Zhouhang Shao, Xuran Wang, Enkai Ji, Shiyang Chen, Jin Wang |
| المصدر: |
IEEE Access, Vol 13, Pp 8963-8976 (2025) |
| بيانات النشر: |
IEEE, 2025. |
| سنة النشر: |
2025 |
| المجموعة: |
LCC:Electrical engineering. Electronics. Nuclear engineering |
| مصطلحات موضوعية: |
Graph neural networks, e-commerce, anomaly detection, heterogeneous graphs, graph attention networks, Electrical engineering. Electronics. Nuclear engineering, TK1-9971 |
| الوصف: |
E-commerce platforms face significant challenges in detecting anomalous products, including counterfeit goods and fraudulent listings, which can undermine user trust and platform integrity. This paper presents Graph Neural Network-based E-commerce Anomaly Detection via Dual-stage Learning (GNN-EADD), a novel approach leveraging graph neural networks for anomaly detection in large-scale e-commerce ecosystems. Our key contributions include: 1) A heterogeneous graph representation incorporating products, sellers, and buyers as nodes with their relationships as edges; 2) A novel dual-stage learning framework combining unsupervised graph embedding with semi-supervised fine-tuning; and 3) An attention mechanism that effectively captures complex patterns within network structures. Extensive experiments on a large-scale Amazon dataset demonstrate that GNN-EADD significantly outperforms state-of-the-art baselines in terms of anomaly detection accuracy, precision, and recall, while showing robustness to various types of anomalies and scalability to large networks. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
2169-3536 |
| Relation: |
https://ieeexplore.ieee.org/document/10829566/; https://doaj.org/toc/2169-3536 |
| DOI: |
10.1109/ACCESS.2025.3526239 |
| URL الوصول: |
https://doaj.org/article/6e308860ae2a445b9b24584d018ecf83 |
| رقم الانضمام: |
edsdoj.6e308860ae2a445b9b24584d018ecf83 |
| قاعدة البيانات: |
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