Conference Agenda

A central task in network analysis is to model social influence, that is, how individual behaviours and outcomes are shaped by their social environment. Classical regression models are not suitable for this purpose, as they frequently rely on independence assumptions that are violated in network data, where individuals' behaviours are inherently interdependent. Although several methods have been proposed to address this problem, existing approaches either treat the network as fixed, rely on multi-step estimation procedures, or are limited to continuous outcome variables.
We introduce the Bayesian logistic actor-attribute latent space model for social influence, a novel approach that jointly models binary actor-level outcomes and the network structure within a unified model framework. The network is represented through a latent social space that provides an interpretable, low-dimensional characterization of the underlying social structure. Our goal is to model a binary actor-level outcome as a function of both observed covariates and latent positions, where the latent social space captures complex network dependencies not explained by covariates alone, but affecting the outcome of interest. Inference is performed within a fully Bayesian framework via a Gibbs sampling algorithm based on Pólya–Gamma data augmentation. This scheme enables principled uncertainty quantification, efficient posterior estimation, and scalability to large networks.