Influence networks: Bayesian modeling and diffusion

Samuel Sánchez-Gutiérrez, Juan Sosa and Carolina Luque

Journal of Applied Statistics, 2026, vol. 53, issue 3, 462-483

Abstract: In this article, we make an innovative adaptation of a Bayesian latent space model based on projections in a novel way to analyze influence networks. By appropriately reparameterizing the model, we establish a formal metric for quantifying each individual's influencing capacity and estimating their latent position embedded in a social space. This modeling approach introduces a novel mechanism for simulating the diffusion of an idea based on the estimated latent characteristics. It assumes that each individual takes the following states: Unknown, undecided, supporting, or rejecting an idea. This approach is demonstrated using an influence network from Twitter (now $ \mathbb {X} $ X) related to the 2022 Tax Reform in Colombia. An exhaustive simulation exercise is also performed to evaluate the proposed diffusion process.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:53:y:2026:i:3:p:462-483

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DOI: 10.1080/02664763.2025.2519139

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