Insights and caveats from mining local and global temporal motifs in cryptocurrency transaction networks
Abstract 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 cur...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-75348-7 |
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| author | Naomi A. Arnold Peijie Zhong Cheick Tidiane Ba Ben Steer Raul Mondragon Felix Cuadrado Renaud Lambiotte Richard G. Clegg |
| author_facet | Naomi A. Arnold Peijie Zhong Cheick Tidiane Ba Ben Steer Raul Mondragon Felix Cuadrado Renaud Lambiotte Richard G. Clegg |
| author_sort | Naomi A. Arnold |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-c3654e44968840e4b43050cdc970170c |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-c3654e44968840e4b43050cdc970170c2024-11-10T12:26:42ZengNature PortfolioScientific Reports2045-23222024-11-0114111310.1038/s41598-024-75348-7Insights and caveats from mining local and global temporal motifs in cryptocurrency transaction networksNaomi A. Arnold0Peijie Zhong1Cheick Tidiane Ba2Ben Steer3Raul Mondragon4Felix Cuadrado5Renaud Lambiotte6Richard G. Clegg7Network Science Institute, Northeastern University LondonSchool of Electronic Engineering and Computer Science, Queen Mary University of LondonSchool of Electronic Engineering and Computer Science, Queen Mary University of LondonPometry LtdSchool of Electronic Engineering and Computer Science, Queen Mary University of LondonSchool of Telecommunications Engineering, Universidad Politecnica de MadridPometry LtdSchool of Electronic Engineering and Computer Science, Queen Mary University of LondonAbstract 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.https://doi.org/10.1038/s41598-024-75348-7 |
| spellingShingle | Naomi A. Arnold Peijie Zhong Cheick Tidiane Ba Ben Steer Raul Mondragon Felix Cuadrado Renaud Lambiotte Richard G. Clegg Insights and caveats from mining local and global temporal motifs in cryptocurrency transaction networks Scientific Reports |
| title | Insights and caveats from mining local and global temporal motifs in cryptocurrency transaction networks |
| title_full | Insights and caveats from mining local and global temporal motifs in cryptocurrency transaction networks |
| title_fullStr | Insights and caveats from mining local and global temporal motifs in cryptocurrency transaction networks |
| title_full_unstemmed | Insights and caveats from mining local and global temporal motifs in cryptocurrency transaction networks |
| title_short | Insights and caveats from mining local and global temporal motifs in cryptocurrency transaction networks |
| title_sort | insights and caveats from mining local and global temporal motifs in cryptocurrency transaction networks |
| url | https://doi.org/10.1038/s41598-024-75348-7 |
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