Mixed-Membership Stochastic Block-Models for Transactional Data
Transactional network data arise in many fields. Although social network models have been applied to transactional data, these models typically assume binary relations between pairs of nodes. We develop a latent mixed membership model capable of modelling richer forms of transactional data. Estimation and inference are accomplished via a variational EM algorithm. Simulations indicate that the learning algorithm can recover the correct generative model. We further present results on a subset of the Enron email dataset. This is joint work with Mahdi Shafiei.
Dr. Hugh Chipman is a Canada Research Chair in Mathematical Modelling at Acadia University, and the director of the Acadia Centre for Mathematical Modelling and Computations. His research focuses on statistical models for extracting information from such large and complex datasets. He completed his PhD studies at the University of Waterloo in 1994, and held a faculty position at the University of Chicago before moving to Acadia. In 2009, he was awarded the CRM-SSC Prize for his outstanding contributions to the application of Bayesian statistical inference for data analysis.
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