Insights and caveats from mining local and global temporal motifs in cryptocurrency transaction networks
CoRR(2024)
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.
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