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Joint Latent Space Model for Social Networks with Multivariate Attributes

Author

Listed:
  • Selena Wang

    (Yale University)

  • Subhadeep Paul

    (The Ohio State University)

  • Paul Boeck

    (The Ohio State University)

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 .

Suggested Citation

  • Selena Wang & Subhadeep Paul & Paul Boeck, 2023. "Joint Latent Space Model for Social Networks with Multivariate Attributes," Psychometrika, Springer;The Psychometric Society, vol. 88(4), pages 1197-1227, December.
    • Handle: RePEc:spr:psycho:v:88:y:2023:i:4:d:10.1007_s11336-023-09926-5
    DOI: 10.1007/s11336-023-09926-5
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    References listed on IDEAS

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