Joint Latent Space Model for Social Networks with Multivariate Attributes

TL;DR

This paper introduces a joint latent space model that integrates social network data with high-dimensional individual attributes, enabling better analysis, visualization, and prediction of complex social structures.

Contribution

It proposes a novel Attribute and Person Latent Space Model (APLSM) with a Variational Bayesian EM algorithm for joint analysis of networks and attributes.

Findings

Revealed divisions among French financial elites based on social and personal attributes.

Demonstrated effective visualization and prediction of social networks and attributes.

Validated the model's ability to uncover underlying social structures.

Abstract

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.