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Bitcoin is the first and highest valued cryptocurrency that storestransactions in a publicly distributed ledger called the blockchain.Understanding the activity and behavior of Bitcoin actors is a crucial researchtopic as they are pseudonymous in the transaction network. In this article, wepropose a method based on taint analysis to extract taint flows --dynamicnetworks representing the sequence of Bitcoins transferred from an initialsource to other actors until dissolution. Then, we apply graph embeddingmethods to characterize taint flows. We evaluate our embedding method withtaint flows from top mining pools and show that it can classify mining poolswith high accuracy. We also found that taint flows from the same period showhigh similarity. Our work proves that tracing the money flows can be apromising approach to classifying source actors and characterizing differentmoney flow patterns

Pattern Analysis of Money Flow in the Bitcoin Blockchain