Exchange Pattern Mining in the Bitcoin Transaction Directed Hypergraph
Bitcoin exchanges operate between digital and fiat currency networks, thus providing an opportunity to connect real-world identities to pseudonymous addresses, an important task for anti-money laundering efforts. We seek to characterize, understand, and identify patterns centered around exchanges in the context of a directed hypergraph model for Bitcoin transactions. We introduce the idea of motifs in directed hypergraphs, considering a particular 2-motif as a potential laundering pattern. We identify distinct statistical properties of exchange addresses related to the acquisition and spending of bitcoin. We then leverage this to build classification models to learn a set of discriminating features, and are able to predict if an address is owned by an exchange with \(>80\%\) accuracy using purely structural features of the graph. Applying this classifier to the 2-motif patterns reveals a preponderance of inter-exchange activity, while not necessarily significant laundering patterns.
KeywordsBitcoin Exchanges Transaction graph Directed hypergraph Motif Classification
This material is based on work supported in part by the Department of Energy National Nuclear Security Administration under Award Number(s) DE-NA0002576. It is also supported in part under the Laboratory Directed Research and Development Program at the Pacific Northwest National Laboratory, a multi-program national laboratory operated by Battelle for the U.S. Department of Energy.
- 1.Anti-money laundering programs for money services businesses, 31 C.F.R. § 1022.210Google Scholar
- 2.Compliance and exemptions, and summons authority, 31 U.S.C. §5318Google Scholar
- 4.FATF: Virtual currencies key definitions and potential AML/CFT risks. Technical report (2014)Google Scholar
- 7.Li, X.-L., Liu, B.: Learning from positive and unlabeled examples with different data distributions. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds.) ECML 2005. LNCS, vol. 3720, pp. 218–229. Springer, Heidelberg (2005). https://doi.org/10.1007/11564096_24 CrossRefGoogle Scholar
- 8.Liu, B., Lee, W.S., Yu, P.S., Li, X.: Partially supervised classification of text documents. In: ICML, vol. 2, pp. 387–394. Citeseer (2002)Google Scholar
- 10.Meiklejohn, S., Pomarole, M., Jordan, G., Levchenko, K., McCoy, D., Voelker, G.M., Savage, S.: A fistful of bitcoins: characterizing payments among men with no names. In: Proceedings of the 2013 conference on Internet measurement conference. pp. 127–140. ACM (2013)Google Scholar
- 12.Möser, M., Böhme, R., Breuker, D.: An inquiry into money launder tools in the bitcoin ecosystem. In: eCrime Researchers Summit, 6–24. Springer, Heidelberg (2013)Google Scholar
- 17.U.S. Department of the Treasury, FinCEN: FinCEN fines ripple labs Inc.: First Civil Enforcement Action Against A Virtual Currency Exchanger. May 2015. https://www.fincen.gov/news/news-releases/fincen-fines-ripple-labs-inc-first-civil-enforcement-action-against-virtual