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.

DeGroot 社交网络意见传播模型可以追溯到 1970 年代，并对信息或虚假信息通过网络传播、改变代理意见的机制进行建模。关于 DeGroot 模型的行为及其在理论社交网络上的变化存在广泛的研究；然而，关于如何使用从观察到的网络扩散过程中收集的数据来估计该模型参数的研究非常有限。现有算法需要大量数据集，而在公共卫生或社会科学应用中通常无法获得这些数据集。为了将意见扩散模型的使用扩展到这些和其他应用，我们开发了一种新的遗传算法，能够使用小数据集恢复 DeGroot 意见扩散过程的参数，包括那些数据缺失且模型参数多于观察到的时间步长的模型。我们证明了该算法对模拟数据和社交网络干预数据的有效性，利用同伴影响来增加采取暴露前预防的意愿，以减少人类免疫缺陷病毒在男男性行为者中的传播。