从加密货币交易网络中挖掘局部与全局时间模体的洞见与警示
Insights and caveats from mining local and global temporal motifs in cryptocurrency transaction networks
AI总结：
本文利用时间模体分析比特币和NFT交易网络，揭示全局模体计数存在误导性，并发现用户模体分布呈重尾、关键参与者签名多样，以及时间分段和完成时间能暴露隐藏的异常活动与行为动态。
摘要：
分布式账本技术已开放了来自比特币和以太坊等加密货币的大量细粒度交易数据。这使得诸如异常检测、反洗钱、模式挖掘和活动聚类等问题的研究成为可能（而传统货币的数据很少可用）。时间网络的形式化方法为表示这些数据提供了一种自然的方式，并提供了大量可用的度量和模型。然而，数据的大规模给使用标准图分析技术带来了挑战。我们使用时间模体来分析两个比特币数据集和一个NFT数据集，采用三个交易序列和最多三个用户。我们表明，常用的简单地在所有用户和所有时间上计数时间模体的技术可能会给出误导性结论。在此我们还研究了每个用户贡献的模体，发现模体分布呈重尾分布，且关键参与者具有多样化的模体签名。我们研究了不同时间段内出现的模体，发现了仅通过对整个数据集进行计数无法看到的事件和异常活动。对模体完成时间的研究揭示了由人类行为以及算法行为驱动的动态。
英文摘要：
Distributed ledger technologies have opened up a wealth of fine-grained transaction data from cryptocurrencies like Bitcoin and Ethereum. This allows research into problems like anomaly detection, anti-money laundering, pattern mining and activity clustering (where data from traditional currencies is rarely available). The formalism of temporal networks offers a natural way of representing this data and offers access to a wealth of metrics and models. However, the large scale of the data presents a challenge using standard graph analysis techniques. We use temporal motifs to analyse two Bitcoin datasets and one NFT dataset, using sequences of three transactions and up to three users. We show that the commonly used technique of simply counting temporal motifs over all users and all time can give misleading conclusions. Here we also study the motifs contributed by each user and discover that the motif distribution is heavy-tailed and that the key players have diverse motif signatures. We study the motifs that occur in different time periods and find events and anomalous activity that cannot be seen just by a count on the whole dataset. Studying motif completion time reveals dynamics driven by human behaviour as well as algorithmic behaviour.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
