Advertisement

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

  • Damiano Di Francesco Maesa
  • Andrea Marino
  • Laura Ricci
Applications

Abstract

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.

Keywords

Bitcoin Blockchain Cryptocurrency Graph analysis 

Notes

Acknowledgements

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.

Compliance with ethical standards

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

References

  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)CrossRefGoogle 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)Google Scholar
  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)Google Scholar
  4. 4.
    Nakamoto, S.: Bitcoin: a peer-to-peer electronic cash system (2008)Google Scholar
  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)Google Scholar
  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)Google Scholar
  7. 7.
    Ober, M., Katzenbeisser, S., Hamacher, K.: Structure and anonymity of the bitcoin transaction graph. Future Internet 5(2), 237–250 (2013)CrossRefGoogle 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)Google Scholar
  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)CrossRefGoogle Scholar
  10. 10.
    Lischke, M., Fabian, B.: Analyzing the bitcoin network: the first four years. Future Internet 8(1), 7 (2016)CrossRefGoogle Scholar
  11. 11.
    Block chain info charts. https://blockchain.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)Google Scholar
  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)Google Scholar
  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)Google Scholar
  15. 15.
    Harrigan, M., Fretter, C.: The unreasonable effectiveness of address clustering. In: 13th IEEE International Conference on Advanced and Trusted Computing (ATC16) (2016)Google Scholar
  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)Google Scholar
  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)Google Scholar
  18. 18.
    US NIST: Descriptions of sha-256, sha-384 and sha-512 (2001)Google Scholar
  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)CrossRefGoogle 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)Google Scholar
  22. 22.
    Dwork, C., Naor, M.: Pricing via processing or combatting junk mail. In: Advances in Cryptology-CRYPTO92, pp. 139–147. Springer (1992)Google Scholar
  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)Google Scholar
  24. 24.
    Miller, A., LaViola Jr., J.J.: Anonymous byzantine consensus from moderately-hard puzzles: a model for bitcoin. http://nakamotoinstitute.org/research/anonymous-byzantine-consensus (2014)
  25. 25.
    Back, A., et al.: Hashcash—a denial of service counter-measure (2002). http://www.hashcash.org/papers/hashcash.pdf
  26. 26.
  27. 27.
    Block chain info tags. https://blockchain.info/tags
  28. 28.
  29. 29.
    Boldi, P., Vigna, S.: Axioms for centrality. Internet Math. 10(3–4):222–262 (2014). http://www.tandfonline.com/doi/abs/10.1080/15427951.2013.865686
  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)Google Scholar
  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)Google Scholar
  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)Google Scholar
  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)Google Scholar
  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)Google Scholar
  35. 35.
    Itai, A., Rodeh, M.: Finding a minimum circuit in a graph. SIAM J. Comput. 7(4), 413–423 (1978)MathSciNetCrossRefzbMATHGoogle Scholar
  36. 36.
    Chiba, N., Nishizeki, T.: Arboricity and subgraph listing algorithms. SIAM J. Comput. 14(1), 210–223 (1985)MathSciNetCrossRefzbMATHGoogle 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)Google Scholar
  38. 38.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of small-world networks. Nature 393(6684), 440–442 (1998)CrossRefzbMATHGoogle Scholar
  39. 39.
    Albert, R., Barabsi, A.L.: Statistical mechanics of complex networks. Rev. Mod. Phys. 74, 47 (2002)MathSciNetCrossRefGoogle Scholar
  40. 40.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: bringing order to the web (1999)Google Scholar
  41. 41.
    Berman, A., Plemmons, R.: Nonnegative matrices in the mathematical sciences. Soc. Ind. Appl. Math. (1994). http://epubs.siam.org/doi/abs/10.1137/1.9781611971262
  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)Google Scholar
  43. 43.
  44. 44.
    Newman, M.E.: The structure and function of complex networks. SIAM Rev. 45(2), 167–256 (2003)MathSciNetCrossRefzbMATHGoogle 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)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of PisaPisaItaly

Personalised recommendations