Abstract
The availability of the entire Bitcoin transaction history, stored in its public blockchain, offers interesting opportunities for analysing the transaction graph to obtain insight on users behaviour. This paper presents an analysis of the Bitcoin users graph, obtained by clustering the transaction graph, to highlight its connectivity structure and the economical meaning of the different obtained components. In fact, the bow tie structure, already observed for the graph of the web, is augmented, in the Bitocoin users graph, with the economical information about the entities involved. We study the connectivity components of the users graph individually, to infer their macroscopic contribution to the whole economy. We define and evaluate a set of measures of nodes inside each component to characterize and quantify such a contribution. We also perform a temporal analysis of the evolution of the resulting bow tie structure. Our findings confirm our hypothesis on the components semantic, defined in terms of their economical role in the flow of value inside the graph.
Highlights
This paper presents an analysis of the Bitcoin users graph, obtained by heuristic clustering of the Bitcoin transaction graph
The information contained in the Blockchain reports the creation dates of each edge, and this can be exploited to perform a set of temporal analysis
We have presented a preliminary evaluation of the Bitcoin User graph connectivity structure in Di Francesco Maesa et al (2018b)
Summary
This paper presents an analysis of the Bitcoin users graph, obtained by heuristic clustering of the Bitcoin transaction graph. In the users graph nodes represent Bitcoin users and edges model the flow of value between them This graph contains information which may be used to conduct rich analyses. The analysis takes inspiration from the seminal paper (Broder et al 2000), introducing the concept of a bow tie structure for the graph representing the Web (subsequently refined in Meusel et al (2014); Donato et al (2008)). In this graph, each node corresponds to a web page and two nodes are connected by a direct arc whether there is an hyperlink from one to the other. By parsing all transactions in the blockchain it is possible to build a transactions graph (representing value exchanges between addresses), that can be refined into an users graph (representing payments between approximated users) by applying the heuristic clustering
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