ArticlePsychometrika2023
Joint Latent Space Model for Social Networks with Multivariate Attributes.
Article in Psychometrika, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
5 citing papers in PubMed.
- Latent space-based network analysis for brain-behavior linking in neuroimaging.Nature methods · 2026Article
- Missing Value Imputation in Relational Data using Variational Inference.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2025Article
- Establishing group-level brain structural connectivity incorporating anatomical knowledge under latent space modeling.Medical image analysis · 2025Article
- A Latent Space Model for HLA Compatibility Networks in Kidney Transplantation.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2022Article
- Recent Integrations of Latent Variable Network Modeling With Psychometric Models.Frontiers in psychology · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
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 .
Indexed as
Identifiers
37615903What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.