Insights and caveats from mining local and global temporal motifs in cryptocurrency transaction networks.

Autor: Arnold NA; Network Science Institute, Northeastern University London, London, E1W 1LP, UK. naomialicearnold@gmail.com.; School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E1 4NS, UK. naomialicearnold@gmail.com., Zhong P; School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E1 4NS, UK., Ba CT; School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E1 4NS, UK.; Department of Computer Science, University of Milan, Via Giovanni Celoria, 18, 20133, Milano, MI, Italy., Steer B; Pometry Ltd, 69 Wilson St, London, EC2A 2BB, UK., Mondragon R; School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E1 4NS, UK., Cuadrado F; School of Telecommunications Engineering, Universidad Politecnica de Madrid, Av. Complutense, 30, Moncloa - Aravaca, 28040, Madrid, Spain., Lambiotte R; Pometry Ltd, 69 Wilson St, London, EC2A 2BB, UK.; Mathematical Institute, University of Oxford, Oxford, OX2 6GG, UK., Clegg RG; School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E1 4NS, UK.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 Nov 04; Vol. 14 (1), pp. 26569. Date of Electronic Publication: 2024 Nov 04.
DOI: 10.1038/s41598-024-75348-7
Abstrakt: 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.
(© 2024. The Author(s).)
Databáze: MEDLINE
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