Bitcoin is a digital currency where transactions between users are recorded on a public ledger, known as the blockchain. On the blockchain, transactions are attributed to anonymous addresses; however, some users---mostly, businesses and organizations---choose to identify themselves with tagged addresses. We explore a subset of tagged transactional data from the Bitcoin blockchain during the first four years of its existence. Our goal is to identify communities of related users and their behavioural spending-patterns. We represent this dataset as a temporal network of users, with weighted edges signifying the transfer of bitcoin amongst users at a certain time. We construct a Bayesian nonparametric mixture model for discovering latent class-structure in transactional data networks. Furthermore, by pooling information within communities of users, this model can be used to summarize the underlying dynamics of the time-series data.