  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.

Methodology