Our work is motivated by a desire to incorporate the vast wealth of social network data into the framework of spatial models. We introduce a method for modeling the spatial correlations that exist over a social network. In particular, we model attributes measured for each member of the network as a continuous process over the social space created by their connections. Our method simultaneously models the unobserved locations of network members in social space and the spatial process that exists over that space based on the observed network connections and nodal attributes. The model is evaluated through simulation studies and applied to the importance ranking for a network of emergency response organizations and the physical activity habits of teenage girls. The introduced methods incorporate network data into the spatial framework, expanding traditional models to include this often relevant source of additional information.

我们的工作是出于将大量社交网络数据纳入空间模型框架的渴望。我们介绍一种对社交网络上存在的空间相关性进行建模的方法。特别是，我们将为网络的每个成员衡量的属性建模为在其联系所创建的社交空间上的连续过程。我们的方法同时基于观察到的网络连接和节点属性，对社交成员在社交空间中未观察到的位置以及该空间上存在的空间过程进行建模。该模型通过仿真研究进行评估，并应用于紧急响应组织和少女身体活动习惯网络的重要性排名。引入的方法将网络数据合并到空间框架中，