  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
