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Author: Admin | 2025-04-28
Place on it, i.e. the flow and accumulation of bitcoins. To characterize the underlying network, we investigate the evolution of basic network characteristics over time, such as the degree distribution, degree correlations and clustering. Concerning the dynamics, we measure the wealth statistics and the temporal patterns of transactions. To explain the observed degree and wealth distribution, we measure the microscopic growth statistics of the system. We provide evidence that preferential attachment is an important factor shaping these distributions. Preferential attachment is often referred to as the “rich get richer” scheme, meaning that hubs grow faster than low-degree nodes. In the case of Bitcoin, this is more than an analogy: we find that the wealth of already rich nodes increases faster than the wealth of nodes with low balance; furthermore, we find positive correlation between the wealth and the degree of a node.ResultsEvolution of the Transaction NetworkBitcoin is an evolving network: new nodes are added by creating new Bitcoin addresses, and links are created if there is a transaction between two previously unconnected addresses. The number of nodes steadily grows over time with some fluctuations; especially noticeable is the large peak which coincides with the first boom in the exchange rate in 2011 (Fig. 1). After five years Bitcoin now has nodes and links. To study the evolution of the network we measure the change of network characteristics in function of time. We identify two distinct phases of growth: (i) The initial phase lasted until the fall of 2010, in this period the system had low activity and was mostly used as an experiment. The network measures are characterized by large fluctuations. (ii) After the initial phase the Bitcoin started to function as a real currency, bitcoins gained real value. The network measures converged to their typical value by mid-2011 and they did not change significantly afterwards. We call this period the trading phase.We first measure the degree distribution of the network. We find that both the in- and the outdegree distributions are highly heterogeneous, and they can be modeled with power-laws [18]. Figures 2 and 3 show the distribution of
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