  Social networks are now ubiquitous and most of them contain interactions
involving multiple actors (groups) like author collaborations, teams or emails
in an organizations, etc. Hypergraphs are natural structures to effectively
capture multi-actor interactions which conventional dyadic graphs fail to
capture. In this work the problem of predicting collaborations is addressed
while modeling the collaboration network as a hypergraph network. The problem
of predicting future multi-actor collaboration is mapped to hyperedge
prediction problem. Given that the higher order edge prediction is an
inherently hard problem, in this work we restrict to the task of predicting
edges (collaborations) that have already been observed in past. In this work,
we propose a novel use of hyperincidence temporal tensors to capture time
varying hypergraphs and provides a tensor decomposition based prediction
algorithm. We quantitatively compare the performance of the hypergraphs based
approach with the conventional dyadic graph based approach. Our hypothesis that
hypergraphs preserve the information that simple graphs destroy is corroborated
by experiments using author collaboration network from the DBLP dataset. Our
results demonstrate the strength of hypergraph based approach to predict higher
order collaborations (size>4) which is very difficult using dyadic graph based
approach. Moreover, while predicting collaborations of size>2 hypergraphs in
most cases provide better results with an average increase of approx. 45% in
F-Score for different sizes = {3,4,5,6,7}.
