GraphGuard：多关系动态图中信用卡欺诈检测的对比自监督学习
GraphGuard: Contrastive Self-Supervised Learning for Credit-Card Fraud Detection in Multi-Relational Dynamic Graphs
AI总结：
针对信用卡欺诈检测中特征工程和标注依赖的局限，提出基于图的对比自监督框架GraphGuard，在真实与合成数据集上验证了其有效性。
摘要：
信用卡欺诈在个人和社会层面都具有重大影响，因此有效的预防至关重要。现有方法严重依赖特征工程和标注信息，而这两者都有显著局限性。在本工作中，我们提出了GraphGuard，一种新颖的基于图的对比自监督框架，用于检测欺诈性信用卡交易。我们在一个真实世界数据集和一个合成数据集上进行了实验。我们的结果为探索基于图的自监督方法在信用卡欺诈检测中的有效性提供了一个有前景的初步方向。
英文摘要：
Credit card fraud has significant implications at both an individual and societal level, making effective prevention essential. Current methods rely heavily on feature engineering and labeled information, both of which have significant limitations. In this work, we present GraphGuard, a novel contrastive self-supervised graph-based framework for detecting fraudulent credit card transactions. We conduct experiments on a real-world dataset and a synthetic dataset. Our results provide a promising initial direction for exploring the effectiveness of graph-based self-supervised approaches for credit card fraud detection.
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