  How social networks influence human behavior has been an interesting topic in
applied research. Existing methods often utilized scale-level behavioral data
to estimate the influence of a social network on human behavior. This study
proposes a novel approach to studying social influence that utilizes item-level
behavioral measures. Under the latent space modeling framework, we integrate
the two interaction maps for respondents' social network data and item-level
behavior measures. The interaction map visualizes the association between the
latent homophily of the respondents and their behaviors measured at the item
level in a low-dimensional latent space, revealing the potential, differential
social influence effects across specific behaviors measured at the item level.
We also measure overall social influence as the impact of the interaction map
configuration contributed by the social network data on the behavior data. The
performance and properties of the proposed approach are evaluated via
simulation studies. We apply the proposed model to an empirical dataset to
demonstrate how the students' friendship network influences their participation
in school activities.
