Scientists have long appreciated the relationship between the social environment and a multitude of individual behaviors. Understanding this relationship presents two statistical challenges: (i) interpretably representing network structure and (ii) linking local network structure to outcomes of interest. This talk explores these challenges in the context of multiview, continuous-time network data. We first present a latent space model for multiview networks, situations where multiple types of relations are observed for the same individuals. We then present preliminary work which develops a Bayesian sparse coding approach for capturing local structure in unstructured observational network data.
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