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Psychometrika
12, 2023;88 (4): 1197-1227.

Joint Latent Space Model for Social Networks with Multivariate Attributes.

Selena Wang, Subhadeep Paul, Paul De Boeck
Author Information
  1. Selena Wang: Department of Biostatistics, Yale University, New Haven, USA. selena.wang@yale.edu. ORCID
  2. Subhadeep Paul: Department of Statistics, The Ohio State University, Columbus, USA.
  3. Paul De Boeck: Department of Statistics, The Ohio State University, Columbus, USA.
PMID: 37615903 DOI: 10.1007/s11336-023-09926-5

Abstract

In social, behavioral and economic sciences, researchers are interested in modeling a social network among a group of individuals, along with their attributes. The attributes can be responses to survey questionnaires and are often high dimensional. We propose a joint latent space model (JLSM) that summarizes information from the social network and the multivariate attributes in a person-attribute joint latent space. We develop a variational Bayesian expectation-maximization estimation algorithm to estimate the attribute and person locations in the joint latent space. This methodology allows for effective integration, informative visualization and prediction of social networks and attributes. Using JLSM, we explore 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 attributes. We analyze user networks and behaviors in multimodal social media systems like YouTube. A R package "jlsm" is developed to fit the models proposed in this paper and is publicly available from the CRAN repository https://cran.r-project.org/web/packages/jlsm/jlsm.pdf .

Keywords

high-dimensional covariates latent space models multimodal networks social networks

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Grants

  1. DMS 1830547/National Science Foundation

MeSH Term

Humans
Bayes Theorem
Psychometrics
Algorithms
Social Networking
Journal Article Research Support, U.S. Gov't, Non-P.H.S.

Word Cloud

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