Joint Latent Space Model for Social Networks with Multivariate Attributes

被引:0
作者
Selena Wang
Subhadeep Paul
Paul De Boeck
机构
[1] Yale University,Department of Biostatistics
[2] The Ohio State University,Department of Statistics
来源
Psychometrika | 2023年 / 88卷
关键词
high-dimensional covariates; multimodal networks; social networks; latent space models;
D O I
暂无
中图分类号
学科分类号
摘要
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.
引用
收藏
页码:1197 / 1227
页数:30
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