Simultaneous and Temporal Autoregressive Network Models

TL;DR

This paper introduces a flexible autoregressive model for dynamic networks that captures both temporal and simultaneous dependencies, overcoming limitations of existing models that assume dyad independence.

Contribution

It proposes a novel observation-driven framework modeling mean and covariance as functions of edge histories, fitting into a generalized linear mixed model structure.

Findings

The model effectively detects simultaneous dependencies in network data.

Simultaneous dependencies increase with coarser time intervals.

The approach performs well in simulation and real-world network analyses.

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 \citep{almquist2014logistic}, 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 which 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 which 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.