Division of Physics and Applied Physics, Nanyang Technological University, 21 Nanyang Link, Singapore, Singapore.
Institute of High Performance Computing, Agency for Science, Technology and Research, 1 Fusionopolis Way, Singapore, Singapore.
PLoS One. 2019 Aug 23;14(8):e0219346. doi: 10.1371/journal.pone.0219346. eCollection 2019.
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.
比特币是最早的加密货币，也是迄今为止最成功的加密货币之一。最近，由于其历史记录的完整性和丰富性，它的动态演变引起了研究社区的关注。在本文中，我们专注于比特币交易的详细演变，旨在阐明驱动比特币交易网络形成的机制。我们的实证研究表明，尽管交易网络的时间特性具有与许多其他网络相同的无标度度分布，但它的形成机制与通常假设的度优先连接或财富优先连接模型不同。通过将每个节点的适应值定义为节点吸引新连接的能力，我们发现观察到的无标度度分布是由于每个节点的内在适应值遵循幂律分布。因此，我们的发现表明，“强者愈强”而不是“富者愈富”的范式在比特币生态系统中起作用。基于这些发现，我们提出了一种通过适应值优先连接和数据驱动的出生/死亡机制来捕捉时间生成过程的模型。我们提出的模型能够产生与从经验比特币网络中获得的结构特性非常吻合的结构特性。
