Daniel Sewell
University of Iowa

Simultaneous and temporal autoregressive network models

While logistic regression models are easily accessible to researchers, when applied to network data there are often very unrealistic assumptions made about the dependence structure of the data.  For temporal networks measured in discrete time, recent work has made good advances (Almquist and Butts, 2014), but there is still the assumption that the dyads are conditionally independent given the edge histories.  This assumption is quite strong and is generally difficult to justify. One would typically expect not only the existence of temporal dependencies through which the network at varying time points are dependent, but also simultaneous dependencies which help determine how the dyads of the network co-evolve.  I propose a general observation driven model for dynamic networks which overcomes this problem by modeling both the mean and the covariance structures as functions of the edge histories using a flexible autoregressive approach.  The proposed methods can be seen as a generalization of the use of random sender/receiver effects, which have a long history in network analysis.  I propose a visualization method which provides evidence concerning the existence of simultaneous dependence.

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