Bitcoin Money Laundering Detection via Subgraph Contrastive Learning
Shiyu Ouyang,
Qianlan Bai,
Hui Feng
et al.
Abstract:The rapid development of cryptocurrencies has led to an increasing severity of money laundering activities. In recent years, leveraging graph neural networks for cryptocurrency fraud detection has yielded promising results. However, many existing methods predominantly focus on node classification, i.e., detecting individual illicit transactions, rather than uncovering behavioral pattern differences among money laundering groups. In this paper, we tackle the challenges presented by the organized, heterogeneous,… Show more
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