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Using a novel genetic algorithm to assess peer influence on willingness to use pre-exposure prophylaxis in networks of Black men who have sex with men. Appl Netw Sci 2021;6(1)

Date

08/24/2021

Pubmed ID

34423110

Pubmed Central ID

PMC8376099

DOI

10.1007/s41109-020-00347-2

Scopus ID

2-s2.0-85102836141   2 Citations

Abstract

The DeGroot model for opinion diffusion over social networks dates back to the 1970s and models the mechanism by which information or disinformation spreads through a network, changing the opinions of the agents. Extensive research exists about the behavior of the DeGroot model and its variations over theoretical social networks; however, research on how to estimate parameters of this model using data collected from an observed network diffusion process is much more limited. Existing algorithms require large data sets that are often infeasible to obtain in public health or social science applications. In order to expand the use of opinion diffusion models to these and other applications, we developed a novel genetic algorithm capable of recovering the parameters of a DeGroot opinion diffusion process using small data sets, including those with missing data and more model parameters than observed time steps. We demonstrate the efficacy of the algorithm on simulated data and data from a social network intervention leveraging peer influence to increase willingness to take pre-exposure prophylaxis in an effort to decrease transmission of human immunodeficiency virus among Black men who have sex with men.

Author List

Johnson KL, Walsh JL, Amirkhanian YA, Borkowski JJ, Carnegie NB

Authors

Yuri A. Amirkhanian PhD Professor in the Psychiatry department at Medical College of Wisconsin
Jennifer L. Walsh PhD Associate Professor in the Psychiatry department at Medical College of Wisconsin