Pattern Analysis of Money Flow in the Bitcoin Blockchain

Natkamon Tovanich (1, 2) , Rémy Cazabet (3, 4, 5, 6)

1 CREST - Centre de Recherche en Économie et Statistique
2 X - École polytechnique
3 DM2L - Data Mining and Machine Learning
4 LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
5 UCBL - Université Claude Bernard Lyon 1
6 IXXI - Institut Rhône-Alpin des systèmes complexes

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.

Mots clés: Bitcoin; Money flow; Taint analysis; Graph embeddings

Domaines: Réseaux sociaux et d'information [cs.SI]; Apprentissage [cs.LG]; Econométrie de la finance [q-fin.ST]

Natkamon Tovanich, Rémy Cazabet. Pattern Analysis of Money Flow in the Bitcoin Blockchain. The 11th International Conference on Complex Networks and their Applications, Nov 2022, Palermo, Italy. pp.443-455, ⟨10.1007/978-3-031-21127-0_36⟩. ⟨hal-03896866⟩