  In many application problems in social, behavioral, and economic sciences,
researchers often have data on a social network among a group of individuals
along with high dimensional multivariate measurements for each individual. To
analyze such networked data structures, we propose a joint Attribute and Person
Latent Space Model (APLSM) that summarizes information from the social network
and the multiple attribute measurements in a person-attribute joint latent
space. We develop a Variational Bayesian Expectation-Maximization estimation
algorithm to estimate the posterior distribution of the attribute and person
locations in the joint latent space. This methodology allows for effective
integration, informative visualization, and prediction of social networks and
high dimensional attribute measurements. Using APLSM, we explore the inner
workings of the French financial elites based on their social networks and
their career, political views, and social status. We observe a division in the
social circles of the French elites in accordance with the differences in their
individual characteristics.
