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In many application problems in social, behavioral, and economic sciences,researchers often have data on a social network among a group of individualsalong with high dimensional multivariate measurements for each individual. Toanalyze such networked data structures, we propose a joint Attribute and PersonLatent Space Model (APLSM) that summarizes information from the social networkand the multiple attribute measurements in a person-attribute joint latentspace. We develop a Variational Bayesian Expectation-Maximization estimationalgorithm to estimate the posterior distribution of the attribute and personlocations in the joint latent space. This methodology allows for effectiveintegration, informative visualization, and prediction of social networks andhigh dimensional attribute measurements. Using APLSM, we explore the innerworkings of the French financial elites based on their social networks andtheir career, political views, and social status. We observe a division in thesocial circles of the French elites in accordance with the differences in theirindividual characteristics.

Joint Latent Space Model for Social Networks with Multivariate Attributes