Social selection models for multilevel networks

Abstract

Social selection models (SSMs) incorporate nodal attributes as explanatory covariates for modelling network ties (Robins et al., 2001). The underlying assumption is that the social processes represented by the graph configurations without attributes are not homogenous, and the network heterogeneity maybe captured by nodal level exogenous covariates. In this article, we propose SSMs for multilevel networks as extensions to exponential random graph models (ERGMs) for multilevel networks (Wang et al., 2013). We categorize the proposed model configurations by their similarities in interpretations arising from complex dependencies among ties within and across levels as well as the different types..