Pattern Analysis of Money Flow in the Bitcoin Blockchain

Natkamon Tovanich () and Rémy Cazabet ()
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Natkamon Tovanich: CREST - Centre de Recherche en Économie et Statistique - ENSAI - Ecole Nationale de la Statistique et de l'Analyse de l'Information [Bruz] - X - École polytechnique - IP Paris - Institut Polytechnique de Paris - ENSAE Paris - École Nationale de la Statistique et de l'Administration Économique - CNRS - Centre National de la Recherche Scientifique, X - École polytechnique - IP Paris - Institut Polytechnique de Paris
Rémy Cazabet: DM2L - Data Mining and Machine Learning - LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information - UL2 - Université Lumière - Lyon 2 - ECL - École Centrale de Lyon - Université de Lyon - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon - INSA Lyon - Institut National des Sciences Appliquées de Lyon - Université de Lyon - INSA - Institut National des Sciences Appliquées - CNRS - Centre National de la Recherche Scientifique, LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information - UL2 - Université Lumière - Lyon 2 - ECL - École Centrale de Lyon - Université de Lyon - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon - INSA Lyon - Institut National des Sciences Appliquées de Lyon - Université de Lyon - INSA - Institut National des Sciences Appliquées - CNRS - Centre National de la Recherche Scientifique, UCBL - Université Claude Bernard Lyon 1 - Université de Lyon, IXXI - Institut Rhône-Alpin des systèmes complexes - ENS de Lyon - École normale supérieure de Lyon - Université de Lyon - UL2 - Université Lumière - Lyon 2 - UJML - Université Jean Moulin - Lyon 3 - Université de Lyon - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon - INSA Lyon - Institut National des Sciences Appliquées de Lyon - Université de Lyon - INSA - Institut National des Sciences Appliquées - Inria - Institut National de Recherche en Informatique et en Automatique - CNRS - Centre National de la Recherche Scientifique - UGA - Université Grenoble Alpes

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Abstract: 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.

Keywords: Bitcoin; Money flow; Taint analysis; Graph embeddings (search for similar items in EconPapers)
Date: 2022-11-08
New Economics Papers: this item is included in nep-mon and nep-pay
Note: View the original document on HAL open archive server: https://hal.science/hal-03896866v1
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Published in 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⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03896866

DOI: 10.1007/978-3-031-21127-0_36

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