  Many real-world systems involving higher-order interactions can be modeled by
hypergraphs, where vertices represent the systemic units and hyperedges
describe the interactions among them. In this paper, we focus on the problem of
hyperlink prediction which aims at inferring missing hyperlinks based on
observed hyperlinks. We propose three similarity indices for hyperlink
prediction based on local random walks and Jensen-Shannon divergence. Numerical
experiments show that the proposed indices outperform the state-of-the-art
methods on a broad range of datasets.
