Bayesian Analysis

Joint Modeling of Longitudinal Relational Data and Exogenous Variables

Rajarshi Guhaniyogi and Abel Rodriguez

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Abstract

This article proposes a framework based on shared, time varying stochastic latent factor models for modeling relational data in which network and node-attributes co-evolve over time. Our proposed framework is flexible enough to handle both categorical and continuous attributes, allows us to estimate the dimension of the latent social space, and automatically yields Bayesian hypothesis tests for the association between network structure and nodal attributes. Additionally, the model is easy to compute and readily yields inference and prediction for missing link between nodes. We employ our model framework to study co-evolution of international relations between 22 countries and the country specific indicators over a period of 11 years.

Article information

Source
Bayesian Anal., Advance publication (2018), 27 pages.

Dates
First available in Project Euclid: 8 June 2019

Permanent link to this document
https://projecteuclid.org/euclid.ba/1559959375

Digital Object Identifier
doi:10.1214/19-BA1160

Keywords
latent factor model nodal attribute social network spike and slab prior systemic dimensions

Rights
Creative Commons Attribution 4.0 International License.

Citation

Guhaniyogi, Rajarshi; Rodriguez, Abel. Joint Modeling of Longitudinal Relational Data and Exogenous Variables. Bayesian Anal., advance publication, 8 June 2019. doi:10.1214/19-BA1160. https://projecteuclid.org/euclid.ba/1559959375


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Supplemental materials

  • Supplementary Material: Joint Modeling of Longitudinal Relational Data and Exogenous Variables. Supplementary material consists of three sections. Section 1 presents the full conditional posteriors for the MCMC algorithm. Section 2 states and proves theorems related to large support of the prior. Section 3 argues identifiability of $q_{S}$, $q_{A}$, $q_{N}$ and $R^{*}$.