![]() Newman, M.E.: The structure and function of complex networks. Cambridge University Press, Cambridge (2008) 151, 1–32 (2014)Ĭatalini, C., Gans, J.S.: Initial coin offerings and the value of crypto tokens, Technical report, National Bureau of Economic Research (2018)īarrat, A., Barthelemy, M., Vespignani, A.: Dynamical Processes on Complex Networks. Wood, G.: Ethereum: a secure decentralised generalised transaction ledger. CoRR (2011)Įndor – inventing the “Google for Predictive Analytics” (2017) 104(18), 7332–7336 (2007)Īltshuler, Y., Aharony, N., Fire, M., Elovici, Y., Pentland, A.: Incremental learning with accuracy prediction of social and individual properties from mobile-phone data. 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. (PNAS) 106, 15274–15278 (2009)Īltshuler, Y., Aharony, N., Pentland, A., Elovici, Y., Cebrian, M.: Stealing reality: when criminals become data scientists (or vice versa). 41(22), 224015 (2008)Įagle, N., Pentland, A., Lazer, D.: Inferring social network structure using mobile phone data. Nature 435(7039), 207–211 (2005)Ĭandia, J., González, M.C., Wang, P., Schoenharl, T., Madey, G., Barabási, A.-L.: Uncovering individual and collective human dynamics from mobile phone records. Nature 453, 779–782 (2008)īarabasi, A.: The origin of bursts and heavy tails in human dynamics. Gonzalez, M.C., Hidalgo, C.A., Barabasi, A.-L.: Understanding individual human mobility patterns. Springer Science & Business Media, New York (2012) White paper (2014)Īltshuler, Y., Elovici, Y., Cremers, A.B., Aharony, N., Pentland, A.: Security and Privacy in Social Networks. Keywordsīuterin, V., et al.: A next-generation smart contract and decentralized application platform. 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. 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. 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. ![]() This work is the first analysis of the network properties of the ERC20 protocol compliant crypto-coins’ trading data. 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. Apart from being a trading ledger for tokens, Blockchain can also be observed as a social network. 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. ![]()
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