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.

翻译：社交网络如何影响人类行为一直是应用研究中的有趣课题。现有方法通常利用量表层面的行为数据来估计社交网络对人类行为的影响。本研究提出了一种利用项目层面行为测量来研究社会影响的新方法。在潜在空间建模框架下，我们整合了受访者社交网络数据和项目层面行为测量的两种交互映射图。交互映射图在低维潜在空间中可视化受访者的潜在同质性与其项目层面测量行为之间的关联，揭示了项目层面特定行为中潜在、差异化的社会影响效应。我们还通过社交网络数据对行为数据的交互映射图配置影响来测量整体社会影响。通过模拟研究评估了所提方法的性能和特性。我们将所提模型应用于实证数据集，以展示学生友谊网络如何影响其学校活动参与。