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Joint Latent Space Model for Social Networks with Multivariate Attributes

Published online by Cambridge University Press:  01 January 2025

Selena Wang Open the ORCID record for Selena Wang [Opens in a new window] ,
Subhadeep Paul  and
Paul De Boeck
Selena Wang*
Affiliation:
Yale University
Subhadeep Paul
Affiliation:
The Ohio State University
Paul De Boeck
Affiliation:
The Ohio State University
*
Correspondence should be made to Selena Wang, Department of Biostatistics, Yale University, New Haven, USA. Email: selena.wang@yale.edu
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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 covariatesmultimodal networkssocial networkslatent space models
Type
Theory & Methods
Information
Psychometrika , Volume 88 , Issue 4 , December 2023 , pp. 1197 - 1227
Copyright
Copyright © 2023 The Author(s) under exclusive licence to The Psychometric Society

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Footnotes

Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/s11336-023-09926-5.

References

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Huang, S., & Feng, Y. (2018). Pairwise covariates-adjusted block model for community detection. arXiv preprint arXiv:1807.03469 Google Scholar
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