发表机构
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出THGT-FD时间异构图变换器，用交易与关系令牌结合Time2Vec编码，在IEEE-CIS数据上取得AUC-ROC 0.8536，验证关系令牌对欺诈风险排序有效。
AI 中文摘要
信用卡欺诈检测通常依赖于表格特征，而重复出现的属性也能提供有用的关系信号。本文提出THGT-FD，一种用于欺诈检测的时间异构图变换器。每笔交易用一个交易令牌和六种类型的关系令牌表示，并将Time2Vec编码融入交易表示中。变换器学习每笔交易内部这些令牌之间的交互，然后输出欺诈概率。实验在从IEEE-CIS欺诈检测数据集中采样的150,000笔交易上进行，并根据TransactionDT按时间顺序划分。在测试集上，THGT-FD取得了0.8536的AUC-ROC、0.4164的平均精度和0.4708的Recall@5%。基于类加权直方图的梯度提升基线取得了0.8722的AUC-ROC。结果表明，关系令牌为欺诈风险排序提供了有用信息，尽管当前模型尚未纳入实体级历史聚合。
英文摘要
Credit card fraud detection typically relies on tabular features, while repeated attributes can also provide useful relational signals. This paper proposes THGT-FD, a Temporal Heterogeneous Graph Transformer for Fraud Detection. Each transaction is represented using one transaction token and six types of relation tokens and incorporates Time2Vec encoding into the transaction representation. A Transformer learns the interactions among these tokens within each individual transaction and then outputs a fraud probability. Experiments were conducted on 150,000 transactions sampled from the IEEE-CIS Fraud Detection dataset and chronologically partitioned according to TransactionDT. On the test set, THGT-FD achieved an AUC-ROC of 0.8536, an average precision of 0.4164, and a Recall@5% of 0.4708. The class-weighted histogram-based gradient-boosting baseline achieved an AUC-ROC of 0.8722. The results indicate that relation tokens provide useful information for fraud-risk ranking, although the current model does not yet incorporate entity-level historical aggregation.
Comments5 pages, 2 figures
