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Forecasting Bitcoin Price with Graph Chainlets

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Advances in Knowledge Discovery and Data Mining (PAKDD 2018)

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Over the last couple of years, Bitcoin cryptocurrency and the Blockchain technology that forms the basis of Bitcoin have witnessed a flood of attention. In contrast to fiat currencies used worldwide, the Bitcoin distributed ledger is publicly available by design. This facilitates observing all financial interactions on the network, and analyzing how the network evolves in time. We introduce a novel concept of chainlets, or Bitcoin subgraphs, which allows us to evaluate the local topological structure of the Bitcoin graph over time. Furthermore, we assess the role of chainlets on Bitcoin price formation and dynamics. We investigate the predictive Granger causality of chainlets and identify certain types of chainlets that exhibit the highest predictive influence on Bitcoin price and investment risk.

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  1. 1.

  2. 2.

  3. 3.

    Some representative chainlets from daily clusters 7, 8, 16 and 35 are \(\mathbb {C}_{9 \rightarrow 11}\), \(\mathbb {C}_{3 \rightarrow 17}\), \(\mathbb {C}_{8 \rightarrow 14}\) and \(\mathbb {C}_{1 \rightarrow 1}\), respectively.


  1. Ahmed, N.K., Neville, J., Rossi, R.A., Duffield, N., Willke, T.L.: Graphlet decomposition: framework, algorithms, and applications. KAIS 50, 1–32 (2016)

    Google Scholar 

  2. Akcora, C.G., Gel, Y.R., Kantarcioglu, M.: Blockchain: a graph primer. arXiv preprint arXiv:1708.08749 (2017)

  3. Androulaki, E., Karame, G.O., Roeschlin, M., Scherer, T., Capkun, S.: Evaluating user privacy in bitcoin. In: Sadeghi, A.-R. (ed.) FC 2013. LNCS, vol. 7859, pp. 34–51. Springer, Heidelberg (2013).

    Chapter  Google Scholar 

  4. Baumann, A., Fabian, B., Lischke, M.: Exploring the bitcoin network. In: WEBIST (1), pp. 369–374 (2014)

    Google Scholar 

  5. Di Battista, G., Di Donato, V., Patrignani, M., Pizzonia, M., Roselli, V., Tamassia, R.: Bitconeview: visualization of flows in the bitcoin transaction graph. In: IEEE VizSec, pp. 1–8 (2015)

    Google Scholar 

  6. Granger, C.W.J.: Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37(3), 424–438 (1969)

    Article  Google Scholar 

  7. Greaves, A., Au, B.: Using the bitcoin transaction graph to predict the price of bitcoin. No Data (2015)

    Google Scholar 

  8. Huang, A.: Similarity measures for text document clustering. In: NZCSRSC, pp. 49–56 (2008)

    Google Scholar 

  9. Jiang, X.F., Chen, T.T., Zheng, B.: Structure of local interactions in complex financial dynamics. Sci. Rep. 4(5321), 1–9 (2014)

    Google Scholar 

  10. Kane, M.J., Price, N., Scotch, M., Rabinowitz, P.: Comparison of ARIMA and random forest time series models for prediction of avian influenza H5N1 outbreaks. BMC Bioinform. 15(1), 276 (2014)

    Article  Google Scholar 

  11. Kondor, D., Csabai, I., Szüle, J., Pósfai, M., Vattay, G.: Inferring the interplay between network structure and market effects in Bitcoin. New J. Phys. 16(12), 125003 (2014)

    Article  Google Scholar 

  12. Kondor, D., Pósfai, M., Csabai, I., Vattay, G.: Do the rich get richer? An empirical analysis of the Bitcoin transaction network. PLOS One 9(2), e86197 (2014)

    Article  Google Scholar 

  13. Lischke, M., Fabian, B.: Analyzing the bitcoin network: the first four years. Future Internet 8(1), 7 (2016)

    Article  Google Scholar 

  14. Madan, I., Saluja, S., Zhao, A.: Automated bitcoin trading via machine learning algorithms (2015)

    Google Scholar 

  15. Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., Alon, U.: Network motifs: simple building blocks of complex networks. Science 298(5594), 824–827 (2002)

    Article  Google Scholar 

  16. Moser, M., Bohme, R., Breuker, D.: An inquiry into money laundering tools in the bitcoin ecosystem. In: eCRS, pp. 1–14. IEEE (2013)

    Google Scholar 

  17. Nakamoto, S.: Bitcoin: a peer-to-peer electronic cash system (2008)

    Google Scholar 

  18. Ober, M., Katzenbeisser, S., Hamacher, K.: Structure and anonymity of the bitcoin transaction graph. Future Internet 5(2), 237–250 (2013)

    Article  Google Scholar 

  19. Portnoff, R.S., Huang, D.Y., Doerfler, P., Afroz, S., McCoy, D.: Backpage and bitcoin: uncovering human traffickers. In: SIGKDD, pp. 1595–1604. ACM (2017)

    Google Scholar 

  20. Sorgente, M., Cibils, C.: The reaction of a network: exploring the relationship between the Bitcoin network structure and the Bitcoin price. No Data (2014)

    Google Scholar 

  21. Tschorsch, F., Scheuermann, B.: Bitcoin and beyond: a technical survey on decentralized digital currencies. IEEE Commun. Surv./Tut. 18(3), 2084–2123 (2016)

    Article  Google Scholar 

  22. White, H., Chalak, K., Lu, X.: Linking granger causality and the pearl causal model with settable systems. In: JMLR, vol. 12, pp. 1–29 (2011)

    Google Scholar 

  23. Yang, S.Y., Kim, J.: Bitcoin market return and volatility forecasting using transaction network flow properties. In: IEEE SSCI, pp. 1778–1785 (2015)

    Google Scholar 

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This research was supported in part by NIH 1R01HG006844, NSF CNS-1111529, CICI-1547324, IIS-1633331, DMS-1736368 and ARO W911NF-17-1-0356.

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Correspondence to Cuneyt G. Akcora .

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Akcora, C.G., Dey, A.K., Gel, Y.R., Kantarcioglu, M. (2018). Forecasting Bitcoin Price with Graph Chainlets. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10939. Springer, Cham.

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  • Print ISBN: 978-3-319-93039-8

  • Online ISBN: 978-3-319-93040-4

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