  Hypernetwork is a useful way to depict multiple connections between nodes,
making it an ideal tool for representing complex relationships in network
science. In recent years, there has been a marked increase in studies on
hypernetworks, however, the comparison of the difference between two
hypernetworks has been given less attention. This paper proposes a
hyper-distance-based method (HD) for comparing hypernetworks. This method takes
into account high-order information, such as the high-order distance between
nodes. The experiments carried out on synthetic hypernetworks have shown that
HD is capable of distinguishing between hypernetworks generated with different
parameters, and it is successful in the classification of hypernetworks.
Furthermore, HD outperforms current state-of-the-art baselines to distinguish
empirical hypernetworks when hyperedges are disrupted.
