  As a natural extension of link prediction on graphs, hyperlink prediction
aims for the inference of missing hyperlinks in hypergraphs, where a hyperlink
can connect more than two nodes. Hyperlink prediction has applications in a
wide range of systems, from chemical reaction networks, social communication
networks, to protein-protein interaction networks. In this paper, we provide a
systematic and comprehensive survey on hyperlink prediction. We propose a new
taxonomy to classify existing hyperlink prediction methods into four
categories: similarity-based, probability-based, matrix optimization-based, and
deep learning-based methods. To compare the performance of methods from
different categories, we perform a benchmark study on various hypergraph
applications using representative methods from each category. Notably, deep
learning-based methods prevail over other methods in hyperlink prediction.
