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Sampling hypergraphs with given degrees

  • M. Dyer
  • , C. Greenhill
  • , P. Kleer
  • , J. Ross
  • , L. Stougie

Research output: Contribution to JournalArticleAcademicpeer-review

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Abstract

There is a well-known connection between hypergraphs and bipartite graphs, obtained by treating the incidence matrix of the hypergraph as the biadjacency matrix of a bipartite graph. We use this connection to describe and analyse a rejection sampling algorithm for sampling simple uniform hypergraphs with a given degree sequence. Our algorithm uses, as a black box, an algorithm A for sampling bipartite graphs with given degrees, uniformly or nearly uniformly, in (expected) polynomial time. The expected runtime of the hypergraph sampling algorithm depends on the (expected) runtime of the bipartite graph sampling algorithm A, and the probability that a uniformly random bipartite graph with given degrees corresponds to a simple hypergraph. We give some conditions on the hypergraph degree sequence which guarantee that this probability is bounded below by a positive constant.
Original languageEnglish
Article number112566
Pages (from-to)1-14
Number of pages14
JournalDiscrete Mathematics
Volume344
Issue number11
Early online date17 Aug 2021
DOIs
Publication statusPublished - Nov 2021

Funding

Research was supported by the Netherlands Organisation for Scientific Research (NWO) through Gravitation Programme Networks 024.002.003 . Martin Dyer is supported by the EPSRC research grant EP/S016562/1 “Sampling in hereditary classes”. Catherine Greenhill is supported by the Australian Research Council Discovery Project DP190100977 . We are grateful to the referees for their helpful comments. Research was supported by the Netherlands Organisation for Scientific Research (NWO) through Gravitation Programme Networks 024.002.003. Martin Dyer is supported by the EPSRC research grant EP/S016562/1 “Sampling in hereditary classes”. Catherine Greenhill is supported by the Australian Research Council Discovery Project DP190100977. Pieter Kleer acknowledges that part of this work was carried out while he was a postdoctoral fellow at the Max Planck Institute for Informatics in Saarbrücken, Germany.

FundersFunder number
Max Planck Institute for Informatics in Saarbrücken
Engineering and Physical Sciences Research CouncilEP/S016562/1
Australian Research CouncilDP190100977
Nederlandse Organisatie voor Wetenschappelijk Onderzoek024.002.003

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