In shaping the Internet of Money, the application of blockchain and distributed ledger technologies (DLTs) to the financial sector triggered regulatory concerns. Notably, while the user anonymity enabled in this field may safeguard privacy and data protection, the lack of identifiability hinders accountability and challenges the fight against money laundering and the financing of terrorism and proliferation (AML/CFT). As law enforcement agencies and the private sector apply forensics to track crypto transfers across ecosystems that are socio-technical in nature, this paper focuses on the growing relevance of these techniques in a domain where their deployment impacts the traits and evolution of the sphere. In particular, this work offers contextualized insights into the application of methods of machine learning and transaction graph analysis. Namely, it analyzes a real-world dataset of Bitcoin transactions represented as a directed graph network through various techniques. The modeling of blockchain transactions as a complex network suggests that the use of graph-based data analysis methods can help classify transactions and identify illicit ones. Indeed, this work shows that the neural network types known as Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) are a promising AML/CFT solution. Notably, in this scenario GCN outperform other classic approaches and GAT are applied for the first time to detect anomalies in Bitcoin. Ultimately, the paper upholds the value of public-private synergies to devise forensic strategies conscious of the spirit of explainability and data openness.

翻译：在塑造“互联网货币”的过程中，区块链与分布式账本技术（DLTs）在金融领域的应用引发了监管层面的担忧。值得注意的是，尽管该领域实现的用户匿名性能够保障隐私与数据安全，但身份不可识别性却阻碍了问责机制，并对打击洗钱、恐怖融资及大规模杀伤性武器扩散（AML/CFT）构成挑战。随着执法机构与私营部门运用法证技术追踪具有社会技术属性的加密货币跨生态系统转移，本文聚焦于这些技术在特定领域日益增长的重要性——其部署正影响着该领域特征与演变。具体而言，本研究为机器学习方法与交易图分析技术的应用提供了情境化洞见。通过运用多种技术手段，本文对以有向图网络形式呈现的比特币交易真实数据集进行了分析。将区块链交易建模为复杂网络的研究表明，基于图的数据分析方法有助于交易分类与非法交易识别。事实上，本研究证实：图卷积网络（GCN）与图注意力网络（GAT）两类神经网络是颇具前景的AML/CFT解决方案。值得注意的是，在此场景下，GCN的表现优于其他经典方法，而GAT则首次被应用于比特币异常检测。最终，本文论证了公私协同在制定兼顾可解释性与数据开放精神的法证策略中的关键价值。