Pattern Analysis of Money Flow in the Bitcoin Blockchain
This work addresses the challenge of understanding pseudonymous Bitcoin actors for researchers and analysts, though it is incremental as it applies existing methods to a specific domain.
The authors tackled the problem of classifying Bitcoin actors by analyzing money flow patterns in the blockchain, using taint analysis and graph embeddings to achieve high accuracy in classifying mining pools.
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