Statistical network models such as the Gaussian Graphical Model and the Ising model have become popular tools to analyze multivariate psychological datasets. In many applications, the goal is to compare such network models across groups. In this paper, I introduce a method to estimate group differences in network models that is based on moderation analysis. This method is attractive because it allows one to make comparisons across more than two groups for all parameters within a single model and because it is implemented for all commonly used cross-sectional network models. Next to introducing the method, I evaluate the performance of the proposed method and existing approaches in a simulation study. Finally, I provide a fully reproducible tutorial on how to use the proposed method to compare a network model across three groups using the R-package mgm.

高斯图模型和伊辛模型等统计网络模型已成为分析多元心理数据集的流行工具。在许多应用中，目标是跨组比较此类网络模型。在本文中，我介绍了一种基于调节分析的网络模型中估计群体差异的方法。这种方法很有吸引力，因为它允许人们对单个模型中的所有参数进行两个以上组的比较，并且因为它适用于所有常用的横截面网络模型。接下来介绍该方法，我在模拟研究中评估了所提出的方法和现有方法的性能。最后，我提供了一个完全可重现的教程，介绍如何使用所提出的方法使用 R 包mgm比较三个组之间的网络模型。