Title: Predicting interactions between individuals with structural and dynamical information,
Abstract: Capturing both the structural and temporal aspects of interactions is crucial
for many real world datasets like contact between individuals. Using the link
stream formalism to capture the dynamic of the systems, we tackle the issue of
activity prediction in link streams, that is to say predicting the number of
links occurring during a given period of time and we present a protocol that
takes advantage of the temporal and structural information contained in the
link stream. Using a supervised learning method, we are able to model the
dynamic of our system to improve the prediction. We investigate the behavior of
our algorithm and crucial elements affecting the prediction. By introducing
different categories of pair of nodes, we are able to improve the quality as
well as increase the diversity of our prediction.