Hypergraph Dissimilarity Measures
This work addresses hypergraph comparison, a domain-specific problem in fields like biology, but appears incremental as it builds on existing graph and tensor methods.
The paper tackles the problem of comparing hypergraphs by proposing two novel approaches: one transforms hypergraphs into graphs for standard dissimilarity measures, and the other uses tensor mathematics to capture multi-way relations, with testing on synthetic and biological datasets.
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.