An adaptive genetic algorithm for fitting DeGroot opinion diffusion models on social networks

Abstract

While a variety of options are available for modeling opinion diffusion--the process through which opinions change and spread through a social network--current methods focus on modeling the process on online social networks where large quantities of opinion data are readily available. For in-person networks, where data are more difficult to collect, models that predict the opinions of the individuals in the network require that the structure of social influence--who is influenced by whom and to what degree--is specified by the researcher instead of informed by data. In order to fit data-driven opinion diffusion models on small networks with limited data, we developed a genetic algorithm for fitting the DeGroot opinion diffusion model. We detail the algorithm and present simulation studies to assess the algorithm's performance. We find the algorithm is able to recover model parameters across a variety of network and data set conditions, it continues to perform well under the assumption violations expected in practical applications, and the algorithm performance is robust to most choices of hyperparameters. Finally, we present an analysis of data from the study that motivated the methodological development.