How human behavior is influenced by a social network that they belong has been an interested 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 by using item-level behavioral measures. Under the latent space modeling framework, we integrate the two latent spaces for respondents' social network data and item-level behavior measures. We then measure social influence as the impact of the latent space 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 explain how students' friendship network influences their participation in school activities.

翻译：人类行为如何受到其所属社会网络的影响一直是应用研究中感兴趣的话题。现有方法经常使用规模级行为数据来估计社会网络对人类行为的影响。本研究提出了使用项目级行为衡量方法研究社会影响的新办法。在潜在空间模型框架下,我们整合了两个潜在空间空间,用于答卷人的社会网络数据和项目级行为计量。然后,我们衡量社会影响,作为社会网络数据对行为数据带来的潜在空间配置的影响。通过模拟研究评估拟议方法的性能和性质。我们将拟议模型应用于经验数据集,解释学生友谊网络如何影响学生参与学校活动。