用图注意力网络识别比特币洗钱交易，效果优于传统方法。
Graph Network Models To Detect Illicit Transactions In Block Chain
- 结合图注意力与残差结构，构建GAT-ResNet模型
- 在椭圆数据集上准确率超越GCN、GAT等基线模型
- 适合反洗钱与金融犯罪监控领域的研究者参考
加密货币的使用导致洗钱等非法活动增加，传统规则方法检测效果下降。本文提出一种新方法，采用带有残差结构的图注意力网络（GAT-ResNet），用于检测区块链中的反洗钱/反恐融资（AML/CFT）相关非法交易。我们在椭圆比特币交易数据集上训练了逻辑回归、随机森林、XGBoost、GCN、GAT及所提GAT-ResNet模型。结果表明，GAT-ResNet在准确率、可靠性与可扩展性方面均优于现有图神经网络模型，展示了图学习技术在打击金融犯罪中的潜力，并为后续研究奠定基础。
原文摘要 · Abstract (English)
The use of cryptocurrencies has led to an increase in illicit activities such as money laundering, with traditional rule-based approaches becoming less effective in detecting and preventing such activities. In this paper, we propose a novel approach to tackling this problem by applying graph attention networks with residual network-like architecture (GAT-ResNet) to detect illicit transactions related to anti-money laundering/combating the financing of terrorism (AML/CFT) in blockchains. We train various models on the Elliptic Bitcoin Transaction dataset, implementing logistic regression, Random Forest, XGBoost, GCN, GAT, and our proposed GAT-ResNet model. Our results demonstrate that the GAT-ResNet model has a potential to outperform the existing graph network models in terms of accuracy, reliability and scalability. Our research sheds light on the potential of graph related machine learning models to improve efforts to combat financial crime and lays the foundation for further research in this area.
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