Network models for social selection processes

Abstract

We present network models for social selection processes, based on the p∗ class of models. Social selection occurs when individuals form social relationships on the basis of certain characteristics they possess. Similarity is a common hypothesis for selection processes, but one that is usually framed dyadically. Structural balance approaches move beyond dyadic conceptualizations and require more sophisticated modeling. The two-block chain graph approach of p∗ social influence models is adapted to allow individual attribute variables to be predictors of network ties. Using a range of dependence assumptions, we present a hierarchy of increasingly complex selection models, including models for continuous attribute measures, which in their simplest form may be assumed to be linear. The models have scope, however, for more complex functional formulations so that more specific hypotheses may be investigated by postulating a particular functional form. Our empirical examples illustrate how dyadic selection may be transmuted into structural effects, and how the absence of dyadic selection may still mask a subtle higher order selection effect as individuals “position” themselves within a wider social environment. In conclusion, we discuss the links between social influence and social selection models.