标题： 时间感知图注意力网络用于加密货币交易欺诈检测 显示英文标题

标题： Temporal-Aware Graph Attention Network for Cryptocurrency Transaction Fraud Detection

摘要： 加密货币交易欺诈检测面临日益复杂的交易模式和严重的类别不平衡的双重挑战。 传统方法依赖于手动特征工程，难以捕捉交易网络中的时间性和结构依赖性。 本文提出了一种增强的时间感知图注意力网络（ATGAT），通过三个模块提升检测性能：（1）设计了一个先进的时间嵌入模块，将多尺度时间差特征与周期位置编码融合；（2）构建了一个时间感知三重注意力机制，联合优化结构、时间和全局上下文注意力；（3）采用加权BCE损失来解决类别不平衡问题。 在Elliptic++加密货币数据集上的实验表明，ATGAT的AUC达到0.9130，相比最佳传统方法XGBoost提高了9.2%，相比GCN提高了12.0%，相比标准GAT提高了10.0%。 该方法不仅验证了时间感知和三重注意力机制对图神经网络的增强效果，还为金融机构提供了更可靠的欺诈检测工具，其设计原则可推广到其他时间图异常检测任务中。 显示英文摘要

摘要： Cryptocurrency transaction fraud detection faces the dual challenges of increasingly complex transaction patterns and severe class imbalance. Traditional methods rely on manual feature engineering and struggle to capture temporal and structural dependencies in transaction networks. This paper proposes an Augmented Temporal-aware Graph Attention Network (ATGAT) that enhances detection performance through three modules: (1) designing an advanced temporal embedding module that fuses multi-scale time difference features with periodic position encoding; (2) constructing a temporal-aware triple attention mechanism that jointly optimizes structural, temporal, and global context attention; (3) employing weighted BCE loss to address class imbalance. Experiments on the Elliptic++ cryptocurrency dataset demonstrate that ATGAT achieves an AUC of 0.9130, representing a 9.2% improvement over the best traditional method XGBoost, 12.0% over GCN, and 10.0% over standard GAT. This method not only validates the enhancement effect of temporal awareness and triple attention mechanisms on graph neural networks, but also provides financial institutions with more reliable fraud detection tools, with its design principles generalizable to other temporal graph anomaly detection tasks.