  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.
