Change we can believe in: Comparing Longitudinal Network Models on Consistency, Interpretability and Predictive Power

While several models for analysing longitudinal network data have been proposed, their main differences, especially regarding the treatment of time, have not been discussed extensively in the literature. However, differences in treatment of time strongly impact the conclusions that can be drawn from data. In this article we compare auto-regressive network models using the example of TERGMs – an extensions of ERGMs – and process-based models using SAOMs as an example. We conclude that the basic TERGM, in contrast to the ERGM, has no consistent micro-level interpretation, and thus only allows interpretation on the level of the network. Further, parameters in the TERGM are strongly dependent on the interval length between two time-points. Neither limitations is true for process-based network models such as the SAOM. Finally, both compared models perform poorly in out-of-sample prediction compared to trivial predictive models.