Abstract
Issuance of cryptocurrencies on top of the Blockchain system by startups and private sector companies is becoming a ubiquitous phenomenon, inducing the trading of these crypto-coins among their holders using dedicated exchanges. Apart from being a trading ledger for tokens, Blockchain can also be observed as a social network. Analyzing and modeling the dynamics of the “social signals” of this network can contribute to our understanding of this ecosystem and the forces acting within. This work is the first analysis of the network properties of the ERC20 protocol compliant crypto-coins’ trading data. Considering all trading wallets as a network’s nodes, and constructing its edges using buy–sell trades, we can analyze the network properties of the ERC20 network. We demonstrate that the network displays strong power-law properties, coinciding with current network theory expectations, however nonetheless, are the first scientific validation of it, for the ERC20 trading data.
The examined data is composed of over 30 million ERC20 tokens trades, performed by over 6.8 million unique wallets, lapsing over a two years period between February 2016 and February 2018.
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Notes
- 1.
We omit these results from the current version, due to space limitations, and they will appear in a future, extended version.
References
Buterin, V., et al.: A next-generation smart contract and decentralized application platform. White paper (2014)
Altshuler, Y., Elovici, Y., Cremers, A.B., Aharony, N., Pentland, A.: Security and Privacy in Social Networks. Springer Science & Business Media, New York (2012)
Gonzalez, M.C., Hidalgo, C.A., Barabasi, A.-L.: Understanding individual human mobility patterns. Nature 453, 779–782 (2008)
Barabasi, A.: The origin of bursts and heavy tails in human dynamics. Nature 435(7039), 207–211 (2005)
Candia, J., González, M.C., Wang, P., Schoenharl, T., Madey, G., Barabási, A.-L.: Uncovering individual and collective human dynamics from mobile phone records. J. Phys. A: Math. Theor. 41(22), 224015 (2008)
Eagle, N., Pentland, A., Lazer, D.: Inferring social network structure using mobile phone data. Proc. Nat. Acad. Sci. (PNAS) 106, 15274–15278 (2009)
Altshuler, Y., Aharony, N., Pentland, A., Elovici, Y., Cebrian, M.: Stealing reality: when criminals become data scientists (or vice versa). In: Intelligent Systems, vol. 26, pp. 22–30. IEEE, November–December 2011
Onnela, J.-P., Saramäki, J., Hyvönen, J., Szabó, G., Lazer, D., Kaski, K., Kertész, J., Barabási, A.-L.: Structure and tie strengths in mobile communication networks. Proc. Nat. Acad. Sci. 104(18), 7332–7336 (2007)
Altshuler, Y., Aharony, N., Fire, M., Elovici, Y., Pentland, A.: Incremental learning with accuracy prediction of social and individual properties from mobile-phone data. CoRR (2011)
Golem (2017)
Endor – inventing the “Google for Predictive Analytics” (2017)
Wood, G.: Ethereum: a secure decentralised generalised transaction ledger. Ethereum Proj. Yellow Pap. 151, 1–32 (2014)
Catalini, C., Gans, J.S.: Initial coin offerings and the value of crypto tokens, Technical report, National Bureau of Economic Research (2018)
Barrat, A., Barthelemy, M., Vespignani, A.: Dynamical Processes on Complex Networks. Cambridge University Press, Cambridge (2008)
Newman, M.E.: The structure and function of complex networks. SIAM Rev. 45(2), 167–256 (2003)
Newman, M.E.: Power laws, pareto distributions and Zipf’s law. Contemp. Phys. 46(5), 323–351 (2005)
Pastor-Satorras, R., Vespignani, A.: Evolution and Structure of the Internet: A Statistical Physics Approach. Cambridge University Press, Cambridge (2007)
Barabasi, A.-L., Oltvai, Z.N.: Network biology: understanding the cell’s functional organization. Nat. Rev. Genet. 5(2), 101 (2004)
Shmueli, E., Mazeh, I., Radaelli, L., Pentland, A.S., Altshuler, Y.: Ride sharing: a network perspective. In: International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, pp. 434–439. Springer (2015)
Altshuler, Y., Puzis, R., Elovici, Y., Bekhor, S., Pentland, A.S.: On the rationality and optimality of transportation networks defense: a network centrality approach. Secur. Transp. Syst. 35–63 (2015)
Altshuler, Y., Fire, M., Aharony, N., Elovici, Y., Pentland, A.: How many makes a crowd? On the correlation between groups’ size and the accuracy of modeling. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, pp. 43–52. Springer (2012)
Altshuler, Y., Fire, M., Shmueli, E., Elovici, Y., Bruckstein, A., Pentland, A.S., Lazer, D.: The social amplifier-reaction of human communities to emergencies. J. Stat. Phys. 152(3), 399–418 (2013)
Altshuler, Y., Pan, W., Pentland, A.: Trends prediction using social diffusion models. In: International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, pp. 97–104. Springer (2012)
Pan, W., Altshuler, Y., Pentland, A.: Decoding social influence and the wisdom of the crowd in financial trading network. In: 2012 International Confernece on Privacy, Security, Risk and Trust (PASSAT) and 2012 International Confernece on Social Computing (SocialCom), pp. 203–209. IEEE (2012)
Shmueli, E., Altshuler, Y., et al.: Temporal dynamics of scale-free networks. In: International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, pp. 359–366. Springer (2014)
Bonneau, J., Miller, A., Clark, J., Narayanan, A., Kroll, J.A., Felten, E.W.: Sok: research perspectives and challenges for bitcoin and cryptocurrencies. In: 2015 IEEE Symposium on Security and Privacy (SP), pp. 104–121. IEEE (2015)
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)
Shrobe, H., Shrier, D.L., Pentland, A.: New Solutions for Cybersecurity. MIT Press, Cambridge (2018)
Ron, D., Shamir, A.: Quantitative analysis of the full bitcoin transaction graph. In: International Conference on Financial Cryptography and Data Security, pp. 6–24. Springer (2013)
Maesa, D.D.F., Marino, A., Ricci, L.: Uncovering the bitcoin blockchain: an analysis of the full users graph. In: 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 537–546. IEEE (2016)
Lischke, M., Fabian, B.: Analyzing the bitcoin network: the first four years. Future Internet 8(1), 7 (2016)
Bartoletti, M., Pompianu, L.: An empirical analysis of smart contracts: platforms, applications, and design patterns. In: International Conference on Financial Cryptography and Data Security, pp. 494–509. Springer (2017)
Anderson, L., Holz, R., Ponomarev, A., Rimba, P., Weber, I.: New kids on the block: an analysis of modern blockchains. arXiv preprint arXiv:1606.06530 (2016)
Christidis, K., Devetsikiotis, M.: Blockchains and smart contracts for the internet of things. IEEE Access 4, 2292–2303 (2016)
Atzei, N., Bartoletti, M., Cimoli, T.: A survey of attacks on ethereum smart contracts (SoK). In: International Conference on Principles of Security and Trust, pp. 164–186. Springer (2017)
Json prc api (2018)
Erdös, P., Rényi, A.: On random graphs, i. Publicationes Mathematicae (Debrecen) 6, 290–297 (1959)
Erdos, P., Renyi, A.: On the evolution of random graphs. Publ. Math. Inst. Hung. Acad. Sci. 5, 17–61 (1960)
Albert, R., Barabási, A.-L.: Statistical mechanics of complex networks. Rev, Modern Phys. 74(1), 47 (2002)
Callaway, D.S., Newman, M.E., Strogatz, S.H., Watts, D.J.: Network robustness and fragility: percolation on random graphs. Phys. Rev. Lett. 85(25), 5468 (2000)
Liu, Y.-Y., Slotine, J.-J., Barabási, A.-L.: Controllability of complex networks. Nature 473(7346), 167 (2011)
Barabási, A.-L.: Linked: The new science of networks (2003)
Barabási, A.-L.: The elegant law that governs us all (2017)
Palla, G., Barabasi, A., Vicsek, T.: Quantifying social group evolution. Nature 446(7136), 664–667 (2007)
Altshuler, Y., Shmueli, E., Zyskind, G., Lederman, O., Oliver, N., Pentland, A.: Campaign optimization through behavioral modeling and mobile network analysis. IEEE Trans. Computat. Soc. Syst. 1(2), 121–134 (2014)
Altshuler, Y., Shmueli, E., Zyskind, G., Lederman, O., Oliver, N., Pentland, A.: Campaign optimization through mobility network analysis. In: Geo-Intelligence and Visualization Through Big Data Trends, pp. 33–74 (2015)
Pentland, A., Altshuler, Y.: Social Physics and Cybercrime. In: New Solutions for Cybersecurity, pp. 351–364. MIT Press (2018)
Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393(6684), 440 (1998)
Borgatti, S.P.: Centrality and network flow. Soc. Netw. 27(1), 55–71 (2005)
Girvan, M., Newman, M.E.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99(12), 7821–7826 (2002)
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Somin, S., Gordon, G., Altshuler, Y. (2018). Network Analysis of ERC20 Tokens Trading on Ethereum Blockchain. In: Morales, A., Gershenson, C., Braha, D., Minai, A., Bar-Yam, Y. (eds) Unifying Themes in Complex Systems IX. ICCS 2018. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-96661-8_45
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