综述图神经网络在网约车反欺诈中的应用与挑战
A Survey on Graph Neural Networks for Fraud Detection in Ride Hailing Platforms
- 梳理图神经网络在网约车反欺诈中的主流架构与方法
- 指出模型在处理数据不平衡和伪装欺诈上的局限性
- 适合研究反欺诈算法或平台安全的工程师与学者
本综述研究了基于图神经网络（GNNs）的网约车平台欺诈检测，聚焦各类模型的有效性。通过分析常见的欺诈行为，系统梳理并对比了现有研究成果，为应对在线网约车平台中的欺诈事件提供参考。论文特别关注类别不平衡与欺诈行为隐蔽性问题，概述了用于异常检测的GNN架构与方法论，揭示了显著的方法进展与研究空白。最后呼吁进一步探索真实场景适用性与技术改进，以提升快速演进的网约车行业中欺诈检测策略的效果。
原文摘要 · Abstract (English)
This study investigates fraud detection in ride hailing platforms through Graph Neural Networks (GNNs),focusing on the effectiveness of various models. By analyzing prevalent fraudulent activities, the research highlights and compares the existing work related to fraud detection which can be useful when addressing fraudulent incidents within the online ride hailing platforms. Also, the paper highlights addressing class imbalance and fraudulent camouflage. It also outlines a structured overview of GNN architectures and methodologies applied to anomaly detection, identifying significant methodological progress and gaps. The paper calls for further exploration into real-world applicability and technical improvements to enhance fraud detection strategies in the rapidly evolving ride-hailing industry.
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