Abstract
Bitcoin and Ethereum transactions present some of the largest real-world complex networks that are publicly available for study, including a detailed picture of their time evolution. As such, they have received a considerable amount of attention from the network science community along with analyses from economic and cryptographic perspectives. Among these studies, in an analysis on the early instance of the Bitcoin network, we have shown the clear presence of the preferential attachment, or the “rich-get-richer” phenomenon. Now, we revisit this question, using a recent version of the Bitcoin network that has grown almost 100-fold since our original analysis. Furthermore, we additionally carry out a comparison with Ethereum, the second most important cryptocurrency. Our results show that preferential attachment continues to be a key factor in the evolution of both the Bitcoin and Ethereum transactoin networks. To facilitate further analysis, we publish a recent version of both transaction networks, and an efficient software implementation that is able to evaluate linking statistics necessary for learn about preferential attachment on networks with several hundred million edges.
Highlights
Cryptocurrencies have presented a disruptive change for both economics and computer science
Cryptocurrencies provide a unique opportunity as financial systems where the whole list of transactions is exposed, making possible to study the dynamic interactions taking place in them (Kondor et al, 2014a; Phetsouvanh et al, 2019; Oggier et al, 2020; Wu et al, 2020); this allows the study of the complete history of how novel, alternative financial systems evolve from their inception (Seebacher and Maleshkova, 2018; Dixon et al, 2019)
Both Bitcoin and Ethereum has experienced a great amount of growth over their lifetime, including multiple “peaks,” where a sudden surge of interest resulted in large upticks of both exchange price and network activity (Figures 1, 2) (Alabi, 2017)
Summary
Cryptocurrencies have presented a disruptive change for both economics and computer science. Interest in cryptocurrencies resulted in a huge amount of money invested in them (Baur et al, 2018; Begušić et al, 2018) and a growing amount of research carried out on diverse application possibilities of the underlying technologies, e.g., blockchain and decentralized trust (Bonneau et al, 2015; Yli-Huumo et al, 2016; Zheng et al, 2016; Seres et al, 2020; Liu et al, 2021). With several booms and busts in price dynamics, there have been a significant amount of interest in understanding and predicting price fluctuations (Kondor et al, 2014b; Akcora et al, 2018; Kurbucz, 2019), and trying to understand cryptocurrency markets based on a comparison with traditional financial instruments (Baur et al, 2018; Begušić et al, 2018).
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