Large Network limit for Latent Variable Process Models with Endogeneously Evolving Interactions among Multiple Types

Abstract

Motivated by the opinion and social dynamics literature, we rigorously study dynamically co-evolving latent space network models, where agent opinions evolve based on a latent state space model influenced by and influencing an underlying dynamically evolving graph determining their interactions. In contrast to earlier work, see [18], agents are allowed to be heterogeneous, i.e., of different types, and their dynamic evolution can capture behaviors like attraction and repulsion between and within groups. In addition, the model encodes bidirectional feedback between the latent process and the underlying interaction network, persistence effects are included, and the interactions are localized, i.e., individual agents do not have global information. We produce weak and empirical limit theorems that encode the asymptotic joint distribution of agent opinion trajectories and their interaction network. In doing so, we describe and exploit the complex dependence structure of the model.