Hypergraph Dissimilarity Measures

• URL: http://arxiv.org/abs/2106.08206v1
• Date: Tue, 15 Jun 2021 15:10:24 GMT
• Title: Hypergraph Dissimilarity Measures
• Authors: Amit Surana, Can Chen and Indika Rajapakse
• Abstract summary: 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.
• Score: 8.890638003061605
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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