  In this paper, we propose two novel approaches for hypergraph comparison. The
first approach transforms the hypergraph into a graph representation for use of
standard graph dissimilarity measures. The second approach exploits the
mathematics of tensors to intrinsically capture multi-way relations. For each
approach, we present measures that assess hypergraph dissimilarity at a
specific scale or provide a more holistic multi-scale comparison. We test these
measures on synthetic hypergraphs and apply them to biological datasets.
