Testing For a Parametric Baseline-Intensity in Dynamic Interaction Networks.

In statistical network analysis it is common to observe so called interaction data. Such data is characterized by the actors who form the vertices of a network. These are able to interact with each other along the edges of the network. One usually assumes that the edges in the network are randomly formed and dissolved over the observation horizon. In addition covariates are observed and the interest is to model the impact of the covariates on the interactions. In this paper we develop a framework to test if a non-parametric form of the baseline intensity allows for more flexibility than a baseline which is parametrically dependent on system-wide covariates (i.e. covariates which take the same value for all individuals, e.g. time). This allows to test if certain seasonality effects can be explained by simple covariates like the time. The procedure is applied to modeling the baseline intensity in a bike-sharing network by using weather and time information.