B1835
Title: Mixed-effects modeling for multiplex social networks
Authors: Nynke Niezink - Carnegie Mellon University (United States) [presenting]
Abstract: Social actors are often embedded in multiple social networks, and there is a growing interest in studying social systems from a multiplex network perspective. A mixed-effects model is proposed for multiplex network data that assumes dyads to be conditionally independent and incorporates dependencies between different network layers via cross-layer dyadic effects and actor random effects. These cross-layer effects, respectively, model the tendencies for ties between two actors and the ties to and from the same actor to be dependent across different relational dimensions. The model also allows for the study of actor and dyad covariate effects. Model parameters are estimated using Bayesian estimation and the choice of priors and the computational faithfulness and inferential properties of the proposed method are evaluated through simulation. An original study that reflects on gossip as perceived by gossip senders and gossip targets, and their differences in perspectives, based on data from 34 Hungarian elementary school classes, highlights the applicability of the proposed method.
