Do the Rich Get Richer? An Empirical Analysis of the Bitcoin Transaction Network

By Dániel Kondor, Márton Pósfai, István Csabai, Gábor Vattay. PLoS ONE, 2013

400 citations recorded by the source corpus.

The possibility to analyze everyday monetary transactions is limited by the scarcity of available data, as this kind of information is usually considered highly sensitive. Present econophysics models are usually employed on presumed random networks of interacting agents, and only some macroscopic properties (e.g. the resulting wealth distribution) are compared to real-world data. In this paper, we analyze Bitcoin, which is a novel digital currency system, where the complete list of transactions is publicly available. Using this dataset, we reconstruct the network of transactions and extract the time and amount of each payment. We analyze the structure of the transaction network by measuring network characteristics over time, such as the degree distribution, degree correlations and clustering. We find that linear preferential attachment drives the growth of the network. We also study the dynamics taking place on the transaction network, i.e. the flow of money. We measure temporal patterns and the wealth accumulation. Investigating the microscopic statistics of money movement, we find that sublinear preferential attachment governs the evolution of the wealth distribution. We report a scaling law between the degree and wealth associated to individual nodes.

Subfields: Economics and Econometrics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/985c93942e6b10e99e34a550eb1446ce0dbbe5e2

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Citations in the corpus

Cryptocurrency trading: a comprehensive survey (2020, background): “[200, 201] firstly published some papers giving analysis on transaction networks on cryptocurrency markets and applied related research in identifying Bitcoin users [202].”
Modern temporal network theory: a colloquium (2015, background): “[146] analyze Bitcoin transactions (with more standard network approach than we discuss in this paper, but the data itself is a temporal network).”
A Survey of Distributed Consensus Protocols for Blockchain Networks (2019, background): “[83] in 2014 shows that the wealth distribution among Bitcoin addresses has been converging to a stable exponential distribution, and the wealth accumulation of node is positively related to its ability to attract new connections, which is another advantage of established well-connected miners.”
On the inefficiency of Bitcoin (2017, background): “Kondor et al. (2014) looked at the structure and evolution of the Bitcoin transaction network.”
Predicting Fluctuations in Cryptocurrency Transactions Based on User Comments and Replies (2016, background): “Such unstable fluctuations have served as an opportunity for speculation for some users while hindering most others from using cryptocurrencies [2, 6, 7].”
Where Is Current Research on Blockchain Technology?—A Systematic Review (2016, background): “The number of transfers and users in the Bitcoin network is constantly increasing [5].”
Motifs in Temporal Networks (2016, neutral): “This dataset consists of all payments made up to October 19, 2014 [11].”
The digital traces of bubbles: feedback cycles between socio-economic signals in the Bitcoin economy (2014, background): “This indicates that identifying users from addresses is important to correctly characterize the Bitcoin ecosystem, which has been overlooked in previous studies [36].”
Bitcoin and Beyond: A Technical Survey on Decentralized Digital Currencies (2016, background): “than the wealth of users with low balance [211].”