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How social networks influence human behavior has been an interesting topic inapplied research. Existing methods often utilized scale-level behavioral datato estimate the influence of a social network on human behavior. This studyproposes a novel approach to studying social influence that utilizes item-levelbehavioral measures. Under the latent space modeling framework, we integratethe two interaction maps for respondents' social network data and item-levelbehavior measures. The interaction map visualizes the association between thelatent homophily of the respondents and their behaviors measured at the itemlevel in a low-dimensional latent space, revealing the potential, differentialsocial influence effects across specific behaviors measured at the item level.We also measure overall social influence as the impact of the interaction mapconfiguration contributed by the social network data on the behavior data. Theperformance and properties of the proposed approach are evaluated viasimulation studies. We apply the proposed model to an empirical dataset todemonstrate how the students' friendship network influences their participationin school activities.

How social networks influence human behavior: An integrated latent space approach for differential social influence