Hypergraph Dissimilarity Measures
Machine Learning
2021-06-16 v1
Abstract
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.
Cite
@article{arxiv.2106.08206,
title = {Hypergraph Dissimilarity Measures},
author = {Amit Surana and Can Chen and Indika Rajapakse},
journal= {arXiv preprint arXiv:2106.08206},
year = {2021}
}