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.

Data-driven analysis of Bitcoin properties: exploiting the users graph / Di Francesco Maesa, Damiano; Marino, Andrea; Ricci, Laura. - In: INTERNATIONAL JOURNAL OF DATA SCIENCE AND ANALYTICS. - ISSN 2364-415X. - STAMPA. - (2018), pp. 63-80. [10.1007/s41060-017-0074-x]

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

Marino, Andrea;
2018

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.
2018
63
80
Di Francesco Maesa, Damiano; Marino, Andrea; Ricci, Laura
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1149244
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