This paper proposes a new two-stage network mediation method based on the use of a latent network approach -- model-based eigenvalue decomposition -- for analyzing social network data with nodal covariates. In the decomposition stage of the observed network, no assumption on the metric of the latent space structure is required. In the mediation stage, the most important eigenvectors of a network are used as mediators. This method further offers an innovative way for controlling for the conditional covariates and it only considers the information left in the network. We demonstrate this approach in a detailed tutorial R code provided for four separate cases -- unconditional and conditional model-based eigenvalue decompositions for either a continuous outcome or a binary outcome -- to show its applicability to empirical network data.

中文翻译：

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使用基于模型的特征值分解的网络中介分析

本文提出了一种新的两阶段网络中介方法，该方法基于潜在网络方法的使用——基于模型的特征值分解——用于分析具有节点协变量的社交网络数据。在观察网络的分解阶段，不需要对潜在空间结构的度量进行假设。在中介阶段，网络最重要的特征向量被用作中介。这种方法进一步提供了一种控制条件协变量的创新方法，它只考虑留在网络中的信息。我们在为四种不同情况提供的详细教程 R 代码中演示了这种方法——连续结果或二元结果的基于无条件和条件模型的特征值分解——以显示其对经验网络数据的适用性。