  We introduce a statistical regression model to investigate the impact of
dyadic relations on complex networks generated from observed repeated
interactions. It is based on generalised hypergeometric ensembles (gHypEG), a
class of statistical network ensembles developed recently to deal with
multi-edge graph and count data. We represent different types of known
relations between system elements by weighted graphs, separated in the
different layers of a multiplex network. With our method, we can regress the
influence of each relational layer, the explanatory variables, on the
interaction counts, the dependent variables. Moreover, we can quantify the
statistical significance of the relations as explanatory variables for the
observed interactions. To demonstrate the power of our approach, we investigate
an example based on empirical data.
