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Hypergraph Dissimilarity Measures

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arxiv 2106.08206 v1 pith:VT5Z2JKA submitted 2021-06-15 cs.LG

Amit Surana , Can Chen , Indika Rajapakse This is my paper · ORCID
classification cs.LG
keywords hypergraphmeasuresapproachdissimilaritycomparisongraphapplyapproaches
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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.

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Cited by 1 Pith paper

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  1. Stability of Hypergraph Invariants and Transformations

    math.MG 2024-12 conditional novelty 6.0 of 10

    Introduces a Gromov-Hausdorff style metric on hypernetworks and proves Lipschitz stability for graphifications, invariant lower bounds, and optimal-transport cost limits.

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