Pattern Analysis of Money Flows 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 - IP Paris - Institut Polytechnique de Paris - 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 a cryptocurrency that stores transaction records in a public distributed ledger called the blockchain. All transactions that occurred since the beginning of Bitcoin in 2009 can therefore be consulted by anyone. This unique dataset allows us to study financial transaction networks among pseudonymous participants. Several works analyzed static transaction networks but did not consider the flow of money over the time. In this work, we focus on the analysis of flows, a challenging task given the scale of the data (hundreds of millions of transactions). We propose a method based on taint analysis to track Bitcoin money flow from initial starting points to the dissolution of the taint. The algorithm derives the dynamics subgraphs passing through known entities in the transaction network. We study the pattern of money flowing from different starting points: we taint coins minted by different mining pools in one day period between 2013 and 2016, and use graph embeddings from three representations of the data: (1) static network, (2) dynamic network, and (3) money flow pattern tree. Both qualitative and quantitative analysis show that mining pools have different diffusion patterns and that those patterns evolve over time. Based on this initial result, we are developing a method to select critical entities and expand our unsupervised approach to characterize other money flow patterns, in particular, related to illegal and cybercrime activities.

Keywords: ฺBitcoin; Money flow; Taint analysis; Graph embeddings (search for similar items in EconPapers)
Date: 2022-07-25
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Published in NetSci 2022: International School and Conference on Network Science, Jul 2022, Shanghai, China

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03898095

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