【Author】
Guo, Dongchao; Dong, Jiaqing; Wang, Kai
【Source】INFORMATION SCIENCES
【Abstract】In recent years, the rapid development of blockchain technologies has attracted considerable attention. However, little effort has been devoted toward investigating the large amount of trade data recorded in blockchains. This paper focuses on transaction data in Ethereum, which is a prominent public blockchain platform supporting not only secure cryptocurrency transfer but also various decentralized applications. By means of the frame- work of network science theory, we find that several transaction features, such as transac-tion volume, transaction relation, and component structure, exhibit a heavy-tailed property and can be approximated by the power law function. In particular, we find that the trans- action relations follow a bow-tie structure with negative assortativity if they are regarded as a directed graph. The popular hubs tend to connect to a large number of common users. We believe that the aforementioned statistics can be ascribed to the vast diversity of trans- actions and the existence of a number of cryptocurrency exchanges. To the best of our knowledge, this study is the first to not only carry out a relatively comprehensive inves-tigation of the transaction data recorded in Ethereum but also probe the statistical laws underlying the transaction relationships from the perspective of network science. (C) 2019 Elsevier Inc. All rights reserved.
【Keywords】Blockchain; Ethereum; Transaction relationships; Heavy tail; Power law
【摘要】近年来，区块链技术的快速发展引起了人们的广泛关注。然而，很少有人致力于调查记录在区块链中的大量贸易数据。本文主要研究以太坊中的交易数据，以太坊是一个著名的公共区块链平台，不仅支持加密货币的安全传输，还支持各种去中心化应用。借助网络科学理论的框架，我们发现交易额、交易关系、成分结构等交易特征具有重尾特性，可以用幂律函数近似。特别地，我们发现当交易关系被视为一个有向图时，它们遵循负关联性的蝴蝶结结构。流行的集线器往往连接到大量的普通用户。我们认为，上述数据可以归因于交易的巨大多样性和许多加密货币交易所的存在。据我们所知，这是第一次从网络科学的角度对以太坊中记录的交易数据进行比较全面的调查，并探究交易关系背后的统计规律。(C) 2019爱思唯尔公司保留所有权利。
【关键词】区块链;以太坊;事务的关系;沉重的尾巴;幂律
评论