Hypergraph Dissimilarity Measures
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 17 of its 58 references that are in the map. First posted on arXiv 15 Jun 2021.
Builds on
17 of 58 references.
References · 17
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5 more
- 2019Deep Hyperedges: a Framework for Transductive and Inductive Learning on Hypergraphscited by 9
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