  While links in simple networks describe pairwise interactions between nodes,
it is necessary to incorporate hypernetworks for modeling complex systems with
arbitrary-sized interactions. In this study, we focus on the hyperlink
prediction problem in hypernetworks, for which the current state-of-art methods
are latent-feature-based. A practical algorithm via topological features, which
can provide understandings of the organizational principles of hypernetworks,
is still lacking. For simple networks, local clustering or loop reflects the
correlations among nodes; therefore, loop-based link prediction algorithms have
achieved accurate performance. Extending the idea to hyperlink prediction faces
several challenges. For instance, what is an effective way of defining loops
for prediction is not clear yet; besides, directly comparing topological
statistics of variable-sized hyperlinks could introduce biases in hyperlink
cardinality. In this study, we address the issues and propose a loop-based
hyperlink prediction approach. First, we discuss and define the loops in
hypernetworks; then, we transfer the loop-features into a hyperlink prediction
algorithm via a simple modified logistic regression. Numerical experiments on
multiple real-world datasets demonstrate superior performance compared to the
state-of-the-art methods.
