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Simultaneous and temporal autoregressive network models

Published online by Cambridge University Press:  12 February 2018

DANIEL K. SEWELL Open the ORCID record for DANIEL K. SEWELL [Opens in a new window]
DANIEL K. SEWELL*
Affiliation:
Department of Biostatistics, University of Iowa, Iowa City, IA 52242, USA (e-mail: daniel-sewell@uiowa.edu)
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Abstract

While logistic regression models are easily accessible to researchers, when applied to network data there are unrealistic assumptions made about the dependence structure of the data. For temporal networks measured in discrete time, recent work has made good advances (Almquist & Butts, 2014), but there is still the assumption that the dyads are conditionally independent given the edge histories. This assumption can be quite strong and is sometimes difficult to justify. If time steps are rather large, one would typically expect not only the existence of temporal dependencies among the dyads across observed time points but also the existence of simultaneous dependencies affecting how the dyads of the network co-evolve. We propose a general observation-driven model for dynamic networks that overcomes this problem by modeling both the mean and the covariance structures as functions of the edge histories using a flexible autoregressive approach. This approach can be shown to fit into a generalized linear mixed model framework. We propose a visualization method that provides evidence concerning the existence of simultaneous dependence. We describe a simulation study to determine the method's performance in the presence and absence of simultaneous dependence, and we analyze both a proximity network from conference attendees and a world trade network. We also use this last data set to illustrate how simultaneous dependencies become more prominent as the time intervals become coarser.

Keywords

dependence structuresdynamic networksgeneralized linear mixed modelsmultivariate probitobservation-driven model
Type
Research Article
Information
Network Science , Volume 6 , Issue 2 , June 2018 , pp. 204 - 231
Copyright
Copyright © Cambridge University Press 2018 

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