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Title: Fitness preferential attachment as a driving mechanism in bitcoin transaction network
Authors: Aspembitova, Ayana
Feng, Ling
Melnikov, Valentin
Chew, Lock Yue
Keywords: Fitness Preferential Attachment
Bitcoin Transaction Network
Issue Date: 2019
Source: Aspembitova, A., Feng, L., Melnikov, V., & Chew, L. Y. (2019). Fitness preferential attachment as a driving mechanism in bitcoin transaction network. PLOS ONE, 14(8), e0219346-. doi:10.1371/journal.pone.0219346
Series/Report no.: PLOS ONE
Abstract: Bitcoin is the earliest cryptocurrency and among the most successful ones to date. Recently, its dynamical evolution has attracted the attention of the research community due to its completeness and richness in historical records. In this paper, we focus on the detailed evolution of bitcoin trading with the aim of elucidating the mechanism that drives the formation of the bitcoin transaction network. Our empirical investigation reveals that although the temporal properties of the transaction network possesses scale-free degree distribution like many other networks, its formation mechanism is different from the commonly assumed models of degree preferential attachment or wealth preferential attachment. By defining the fitness value of each node as the ability of the node to attract new connections, we have instead uncovered that the observed scale-free degree distribution results from the intrinsic fitness of each node following a power-law distribution. Our finding thus suggests that the “good-get-richer” rather than the “rich-get-richer” paradigm operates within the bitcoin ecosystem. Based on these findings, we propose a model that captures the temporal generative process by means of a fitness preferential attachment and data-driven birth/death mechanism. Our proposed model is able to produce structural properties in good agreement with those obtained from the empirical bitcoin network.
DOI: 10.1371/journal.pone.0219346
Rights: © 2019 Aspembitova et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Fulltext Permission: open
Fulltext Availability: With Fulltext
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