评估基于启发式方法的 Bitcoin 地址聚类有效性
Assessing the Efficacy of Heuristic-Based Address Clustering for Bitcoin
AI总结：
本文研究 Bitcoin 实体聚类，评估两种常用启发式并提出四种新启发式，通过聚类比率衡量实体缩减并分析其随时间的演变。
摘要：
探索 Bitcoin 区块链中的交易，需要考察数亿个实体之间的 bitcoin 转移。然而，研究如此庞大数量的实体通常并不现实，而且会消耗大量资源。因此，在大多数分析研究中，实体聚类都是初始步骤。这一过程通常采用基于这些实体的实践与行为的启发式方法。在本研究中，我们深入考察了两种广泛使用的启发式方法，同时提出四种新的启发式方法。我们的贡献包括引入 clustering ratio（聚类比率），这是一种旨在量化给定启发式方法所实现的实体数量缩减程度的指标。评估这一缩减比率，对于证明为分析目的选择某一特定启发式方法的合理性具有重要作用。鉴于 Bitcoin 系统的动态特性——其特征是区块链上实体数量持续增加，以及这些实体的行为不断演变——我们扩展了研究，以探讨每种启发式方法的聚类比率随时间的演变。这一时间分析增强了我们对这些启发式方法随时间变化的有效性的理解。
英文摘要：
Exploring transactions within the Bitcoin blockchain entails examining the transfer of bitcoins among several hundred million entities. However, it is often impractical and resource-consuming to study such a vast number of entities. Consequently, entity clustering serves as an initial step in most analytical studies. This process often employs heuristics grounded in the practices and behaviors of these entities. In this research, we delve into the examination of two widely used heuristics, alongside the introduction of four novel ones. Our contribution includes the introduction of the \textit{clustering ratio}, a metric designed to quantify the reduction in the number of entities achieved by a given heuristic. The assessment of this reduction ratio plays an important role in justifying the selection of a specific heuristic for analytical purposes. Given the dynamic nature of the Bitcoin system, characterized by a continuous increase in the number of entities on the blockchain, and the evolving behaviors of these entities, we extend our study to explore the temporal evolution of the clustering ratio for each heuristic. This temporal analysis enhances our understanding of the effectiveness of these heuristics over time.
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