Data-driven analysis of Bitcoin properties: exploiting the users graph


Data analytic has recently enabled the uncovering of interesting properties of several complex networks. Among these, it is worth considering the bitcoin blockchain, because of its peculiar characteristic of reflecting a niche, but also a real economy whose transactions are publicly available. In this paper, we present the analyses we have performed on the users graph inferred from the bitcoin blockchain, dumped in December 2015, so after the occurrence of the exponential explosion in the number of transactions. We first present the analysis assessing classical graph properties like densification, distance analysis, degree distribution, clustering coefficient and several centrality measures. Then, we analyse properties strictly tied to the nature of bitcoin, like rich-get-richer property, which measures the concentration of richness in the network.

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  1. 1.

    Komurov, K., Gunes, M.H., White, M.A.: Fine-scale dissection of functional protein network organization by statistical network analysis. PLoS ONE 4(6), e6017 (2009)

    Article  Google Scholar 

  2. 2.

    Cheung, D., Gunes, M.H.: A complex network analysis of the united states air transportation. In: Proceedings IEEE/ACM ASONAM, pp. 699–701. Washington, DC (2012)

  3. 3.

    Kardes, H., Sevincer, A., Gunes, M., Yuksel, M.: Six degrees of separation among US researchers. In: Proceedings of IEEE/ACM SONAM, pp. 654–659 (2012)

  4. 4.

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

  5. 5.

    Ron, D., Shamir, A.: Quantitative analysis of the full bitcoin transaction graph. In: Financial Cryptography and Data Security—17th International Conference, FC, Okinawa, Japan, April 1–5, 2013. Revised Selected Papers 2013, pp. 6–24 (2013)

  6. 6.

    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 Internet Measurement Conference, IMC 2013, Barcelona, Spain, 23–25 Oct 2013, pp. 127–140 (2013)

  7. 7.

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

    Article  Google Scholar 

  8. 8.

    Androulaki, E., Karame, G., Roeschlin, M., Scherer, T., Capkun, S.: Evaluating user privacy in bitcoin. In: Financial Cryptography and Data Security—17th International Conference, FC, Okinawa, Japan, 1–5 Apr 2013. Revised Selected Papers 2013, pp. 34–51 (2013)

  9. 9.

    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 

  10. 10.

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

    Article  Google Scholar 

  11. 11.

    Block chain info charts.

  12. 12.

    Maesa, D.D.F., Marino, A., Ricci, L.: Uncovering the bitcoin blockchain: an analysis of the full users graph. In IEEE DSAA 2016, 3rd IEEE International Conference on Data Science and Advanced Analytics, Montreal, October (2016)

  13. 13.

    Fergal, R., Harrigan, M.: An analysis of anonymity in the bitcoin system. In: Proceeding of 2011 PASSAT/SocialCom 2011, pp. 1318–1326. IEEE (2011)

  14. 14.

    Ruffing, T., Moreno-Sanchez, P., Kate, A.: Coinshuffle: Practical decentralized coin mixing for bitcoin. In: Computer Security-ESORICS, pp. 345–364. Springer (2014)

  15. 15.

    Harrigan, M., Fretter, C.: The unreasonable effectiveness of address clustering. In: 13th IEEE International Conference on Advanced and Trusted Computing (ATC16) (2016)

  16. 16.

    Popuri, M.K., Gunes, M.H.: Empirical analysis of crypto currencies. In: 7th Workshop on Complex Networks (CompleNet), Dijon, France, Mar 23–25 (2016)

  17. 17.

    Maesa, D.D.F., Marino, A., Ricci, L.: An analysis of the bitcoin users graph: inferring unusual behaviours. In: Proceedings of the 5-th International Workshop on Complex Networks and their Applications, Milan (2016)

  18. 18.

    US NIST: Descriptions of sha-256, sha-384 and sha-512 (2001)

  19. 19.

    Preneel, B., Bosselaers, A., Dobbertin, H.: The cryptographic hash function RIPEMD-160. CryptoBytes 3(2), 9–14 (1997)

    Google Scholar 

  20. 20.

    Johnson, D., Menezes, A., Vanstone, S.: The elliptic curve digital signature algorithm (ECDSA). Int. J. Inf. Secur. 1(1), 36–63 (2001)

    Article  Google Scholar 

  21. 21.

    Merkle, R.C.: A digital signature based on a conventional encryption function. In: Proceedings of Advances in Cryptology—CRYPTO ’87, Santa Barbara, California, USA, 16–20 Aug 1987, pp. 369–378 (1987)

  22. 22.

    Dwork, C., Naor, M.: Pricing via processing or combatting junk mail. In: Advances in Cryptology-CRYPTO92, pp. 139–147. Springer (1992)

  23. 23.

    Garay, J., Kiayias, A., Leonardos, N.: The bitcoin backbone protocol: Analysis and applications. In: Annual International Conference on the Theory and Applications of Cryptographic Techniques, pp. 281–310. Springer (2015)

  24. 24.

    Miller, A., LaViola Jr., J.J.: Anonymous byzantine consensus from moderately-hard puzzles: a model for bitcoin. (2014)

  25. 25.

    Back, A., et al.: Hashcash—a denial of service counter-measure (2002).

  26. 26.


  27. 27.

    Block chain info tags.

  28. 28.

    Wallet explorer.

  29. 29.

    Boldi, P., Vigna, S.: Axioms for centrality. Internet Math. 10(3–4):222–262 (2014).

  30. 30.

    Boldi, P., Vigna, S.: The webgraph framework I: compression techniques. In: Proceedings of the 13th International Conference on World Wide Web, ser. WWW ’04, pp. 595–602. ACM (2004)

  31. 31.

    Boldi, P., Rosa, M., and Vigna, S.: Hyperanf: Approximating the neighbourhood function of very large graphs on a budget. In: Proceedings of the 20th International Conference on World Wide Web, pp. 625–634. ACM (2011)

  32. 32.

    Borassi, M., Crescenzi, P., Habib, M., Kosters, W.A., Marino, A., Takes, F.W.: On the solvability of the six degrees of Kevin Bacon game—a faster graph diameter and radius computation method. In: Fun with Algorithms—7th International Conference, FUN 2014, Lipari Island, Sicily, Italy, 1–3 July 2014. Proceedings, pp. 52–63 (2014)

  33. 33.

    Leskovec, J., Kleinberg, J., Faloutsos, C.: Graphs over time: densification laws, shrinking diameters and possible explanations. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 177–187. ACM (2005)

  34. 34.

    Backstrom, L., Boldi, P., Rosa, M., Ugander, J., Vigna, S.: Four degrees of separation. In: Proceedings of the 4th Annual ACM Web Science Conference, pp. 33–42. ACM (2012)

  35. 35.

    Itai, A., Rodeh, M.: Finding a minimum circuit in a graph. SIAM J. Comput. 7(4), 413–423 (1978)

    MathSciNet  Article  MATH  Google Scholar 

  36. 36.

    Chiba, N., Nishizeki, T.: Arboricity and subgraph listing algorithms. SIAM J. Comput. 14(1), 210–223 (1985)

    MathSciNet  Article  MATH  Google Scholar 

  37. 37.

    Becchetti, L., Boldi, P., Castillo, C., Gionis, A.: Efficient semi-streaming algorithms for local triangle counting in massive graphs. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 16–24. ACM (2008)

  38. 38.

    Watts, D.J., Strogatz, S.H.: Collective dynamics of small-world networks. Nature 393(6684), 440–442 (1998)

    Article  MATH  Google Scholar 

  39. 39.

    Albert, R., Barabsi, A.L.: Statistical mechanics of complex networks. Rev. Mod. Phys. 74, 47 (2002)

    MathSciNet  Article  Google Scholar 

  40. 40.

    Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: bringing order to the web (1999)

  41. 41.

    Berman, A., Plemmons, R.: Nonnegative matrices in the mathematical sciences. Soc. Ind. Appl. Math. (1994).

  42. 42.

    Boldi, P., Vigna, S.: In-core computation of geometric centralities with hyperball: a hundred billion nodes and beyond. In: Proceedings of the 13th IEEE International Conference on Data Mining Workshops (ICDM), pp. 621–628 (2013)

  43. 43.

    Current standard for dust limit.

  44. 44.

    Newman, M.E.: The structure and function of complex networks. SIAM Rev. 45(2), 167–256 (2003)

    MathSciNet  Article  MATH  Google Scholar 

  45. 45.

    Borassi, M., Coudert, D., Crescenzi, P., Marino, A.: On computing the hyperbolicity of real-world graphs. In: Algorithms—ESA 2015—23rd Annual European Symposium, Patras, Greece, 14–16 Sept 2015, Proceedings, pp. 215–226 (2015)

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The authors would like to thank Dr. Christian Decker and Prof. Roger Wattenhofer of the Distributed Computing Group, ETH Zurich for providing us the blockchain in Protocol Buffers format. This work was supported by PRA, Progetto di Ricerca di Ateneo, “Big Data, Social Mining and Risk Management”, University of Pisa.

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Correspondence to Damiano Di Francesco Maesa.

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Di Francesco Maesa, D., Marino, A. & Ricci, L. Data-driven analysis of Bitcoin properties: exploiting the users graph. Int J Data Sci Anal 6, 63–80 (2018).

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  • Bitcoin
  • Blockchain
  • Cryptocurrency
  • Graph analysis