2022/07/15 by Natkamon Tovanich, Tovanich, Natkamon, Rémy Cazabet +1
Computer Science · Physics and Astronomy · #Blockchain Technology Applications and Security #Complex Network Analysis Techniques #FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (cs.LG) #Social and Information Networks (cs.SI) #Statistical Finance (q-fin.ST)
paper · pdf · doi:10.48550/arxiv.2207.07315
openalex publication_date 2022/07/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Bitcoin is the first and highest valued cryptocurrency that stores transactions in a publicly distributed ledger called the blockchain. Understanding the activity and behavior of Bitcoin actors is a crucial research topic as they are pseudonymous in the transaction network. In this article, we propose a method based on taint analysis to extract taint flows --dynamic networks representing the sequence of Bitcoins transferred from an initial source to other actors until dissolution. Then, we apply graph embedding methods to characterize taint flows. We evaluate our embedding method with taint flows from top mining pools and show that it can classify mining pools with high accuracy. We also found that taint flows from the same period show high similarity. Our work proves that tracing the money flows can be a promising approach to classifying source actors and characterizing different money flow patterns