Ethereum transaction tracking: Inferring evolution of transaction networks via link prediction
【Author】 Lin, Dan; Wu, Jiajing; Xuan, Qi; Tse, Chi K.
【Source】PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
【影响因子】3.778
【Abstract】Blockchain is an emerging technology which has attracted wide attention in recent years. As one of the blockchain applications, cryptocurrency has developed rapidly in recent years, attracting criminals to commit fraud and money laundering. Therefore, to better protect the legitimate interests of users and help formulate an effective supervision, it is necessary to track and follow transaction records on blockchain-based systems. This paper studies the problem of transaction tracking in Ethereum from a network perspective, aiming to study explainable strategies for money flow generation. We first collect the space-intensive transaction data from Ethereum blockchain and model them as temporal weighted multi-digraphs. A variety of tracking strategies considering different transaction factors (i.e., frequency and amount) are proposed, and the corresponding random-walk based link predictions method are designed for evaluation. Our method gets explainable results from the experiments, demonstrating that both transaction frequency and amount influence the generation of new transactions in Ethereum. This means when tracking the money flow among Ethereum accounts, we should pay more attention to those transaction paths having a shorter time interval and a larger amount. From these transaction features, the proposed random-walk based link prediction framework is found to be an effective method for transaction tracking. Furthermore, we show an application of transaction tracking via link prediction effectively enhance the ability to detect the suspicious accounts in Ethereum. (C) 2022 Published by Elsevier B.V.
【Keywords】Ethereum; Complex networks; Cryptocurrency; Transaction tracking; Link prediction; Network evolution
【发表时间】2022 15-Aug
【收录时间】2022-08-15
【文献类型】实证数据
【主题类别】
区块链治理-技术治理-链上交易追溯
wangjiaxin
今日有1篇链上数据分析相关文章，https://doi.org/10.1016/j.physa.2022.127504，发表在《PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS》上。本文从网络角度研究了以太坊的交易跟踪问题。首先从以太坊区块链收集交易数据，并将其建模为时间加权的网络图，并提出了考虑不同交易因素（即频率和数量）的各种跟踪策略，设计了相应的基于随机行走的链接预测方法进行评估。我们的方法从实验中获得可解释的结果，表明交易频率和金额都会影响以太坊中新交易的生成。这意味着在跟踪以太坊账户之间的资金流时，我们应该更多地关注那些时间间隔更短、金额更大的交易路径。
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