Arnold Naomi A, Zhong Peijie, Ba Cheick Tidiane, Steer Ben, Mondragon Raul, Cuadrado Felix, Lambiotte Renaud, Clegg Richard G
Network Science Institute, Northeastern University London, London, E1W 1LP, UK.
School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E1 4NS, UK.
Sci Rep. 2024 Nov 4;14(1):26569. doi: 10.1038/s41598-024-75348-7.
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.
分布式账本技术已经从比特币和以太坊等加密货币中挖掘出了大量细粒度的交易数据。这使得人们能够研究异常检测、反洗钱、模式挖掘和活动聚类等问题（而传统货币的数据很少能用于此类研究）。时态网络的形式体系为表示这些数据提供了一种自然的方式，并且可以使用大量的指标和模型。然而，数据的规模给使用标准图分析技术带来了挑战。我们使用时态模式来分析两个比特币数据集和一个非同质化代币（NFT）数据集，使用三笔及以上交易且涉及多达三个用户的序列。我们表明，简单地对所有用户和所有时间的时态模式进行计数这种常用技术可能会得出误导性的结论。在此，我们还研究了每个用户贡献的模式，发现模式分布是重尾的，并且关键参与者具有不同的模式特征。我们研究了在不同时间段出现的模式，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。对模式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变化。
