Bayesian Analysis of Dynamic Network Regression with Joint Edge/Vertex Dynamics
Published in Bayesian Inference in the Social Sciences, 2014
Abstract
Change in network structure and composition has been a topic of extensive theoretical and methodological interest over the last two decades; however, the effects of endogenous group change on interaction dynamics within the context of social networks is a surprisingly understudied area. Network dynamics may be viewed as a process of change in the edge structure of a network, in the vertex set on which edges are defined, or in both simultaneously. Recently, Almquist and Butts introduced a framework for inference on network panel data with vertex dynamics – called dynamic network logistic regression – a subfamily of temporal exponential-family random graph model (TERGM). Here, we expand this approach by exploring Bayesian methods for estimation and model assessment. We propose and implement techniques for Bayesian inference via both MAP and MCMC under several different priors, with an emphasis on easily used, minimally informative priors that may be employed in a range of settings. These different approaches are compared in terms of model fit and predictive model assessment using several reference data sets.
Recommended citation: Almquist, Z. W., & Butts, C. T. (2014). "Bayesian Analysis of Dynamic Network Regression with Joint Edge/Vertex Dynamics." In Bayesian Inference in the Social Sciences. Ed. by I. Jeliazkov and X.-S. Yang. Hoboken, New Jersey: John Wiley & Sons.
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