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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. There exists extensive research about the behavior of the DeGroot models and its variations over theoretical social networks with specified characteristics; however, research on how to fit these models using data collected from an observed network diffusion process is much more limited. Because of these current limitations, DeGroot models and their variants are an untapped resource for a variety of applications.
Current methods require more time points than agents or at least 100 time points, making them impractical for use in applications where data collection is costly. We detail the operators used to create a novel genetic algorithm capable of recovering the parameters of a DeGroot opinion diffusion model, even in cases with missing data and more parameters than data points. The efficacy of the algorithm will be demonstrated on both a generated data set with known parameters and on a data set investigating the process by which minority beliefs become majority beliefs.
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