A Bayesian Nonparametric Model for Community Discovery on the Bitcoin Transaction Network

• Activity Number: 8 - Computational Methods and Bayesian Inference for Networks
• Type: Invited
• Date/Time: Sunday, July 29, 2018 : 2:00 PM to 3:50 PM
• Sponsor: Council of Chapters
• Author(s): Creagh Briercliffe* and Alexandre Bouchard-Côté and Paul Gustafson
• Companies: University of British Columbia and University of British Columbia and University of British Columbia

Abstract

Bitcoin is a digital currency where transactions between users are recorded on a public ledger, known as the blockchain. We explore a subset of 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. To that end, 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, we approximate the posterior distribution of user partitions using a Metropolis-Hastings algorithm.