发表机构
University of California, Los Angeles（加州大学洛杉矶分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种基于穿透模板的可解释方法，通过最大基数最大权重匹配和Edmonds算法检测交易图中的洗钱分层模式，并在以太坊数据上识别出283个实例及受制裁地址。
AI 中文摘要
分层是洗钱过程中的一个关键阶段，在此阶段，资产通过中间实体转移以掩盖其来源。我们将这种转移建模为交易图，即一种带标签的有向多重图，其中节点代表实体（如个人或组织），边代表它们之间的交易。本研究旨在解决交易图中分层模式的检测问题。我们定义了穿透模板，这是一类交易图，其结构指示了特定的分层模式，即实体接收资金并迅速转出。我们将在大规模交易图中检测这些模板实例的问题表述为最大基数、最大权重匹配问题。所提出的方法使用Edmonds开花算法，该算法可证明找到该问题的最优解。本文对公开可用的以太坊交易数据进行了案例研究。我们识别出283个穿透模板实例，这些实例通过共享地址节点串联成一个密集互联的网络，该网络转移了数亿美元的稳定币价值。我们发现，识别出的网络中有六个地址因与犯罪组织有关联而受到美国财政部制裁，这为网络与受制裁犯罪实体之间的联系提供了证据。
英文摘要
Layering is a key stage of the money laundering process in which assets are moved through intermediate entities to obscure their origin. We model this movement as a transaction graph, a labeled directed multigraph where nodes represent entities, such as individuals or organizations, and edges represent transactions between them. This work addresses the detection of layering patterns in transaction graphs. We define pass-through templates, a class of transaction graphs whose structure is indicative of a specific layering pattern in which entities receive funds and rapidly forward them. We formulate detecting instances of these templates within a larger transaction graph as a maximum-cardinality, maximum-weight matching problem. The resulting method uses Edmonds' blossom algorithm, which provably finds an optimal solution to this problem. Here we present a case study on publicly available Ethereum transaction data. We identify 283 pass-through template instances, chained through shared address nodes into a densely interconnected network that moves hundreds of millions of dollars in stablecoin value. We find that six addresses in the identified network are sanctioned by the U.S. Department of the Treasury for associations with criminal organizations, providing evidence of ties between the network and sanctioned criminal entities.
