Perfect Matchings and Loose Hamilton Cycles in the Semirandom Hypergraph Model

ABSTRACT We study the 2‐offer semirandom 3‐uniform hypergraph model on vertices. At each step, we are presented with 2 uniformly random vertices. We choose any other vertex, thus creating a hyperedge of size 3. We show a strategy that constructs a perfect matching and another that constructs a loose Hamilton cycle, both succeeding asymptotically almost surely within steps. Both results extend to ‐uniform hypergraphs. Our methods are qualitatively different from those that have been used for semirandom graphs. Much of the analysis is done on an auxiliary graph that is a uniform ‐out subgraph of a random bipartite graph, and this tool may be useful in other contexts.

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Publication Details

Journal
Random Structures and Algorithms
Published
2025-03-01
DOI
https://doi.org/10.1002/rsa.21270
Citations
1
Primary Topic
Limits and Structures in Graph Theory
Type
article
Field-Weighted Citation Impact
5.33

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article

Perfect Matchings and Loose Hamilton Cycles in the Semirandom Hypergraph Model

Michael Molloy, Paweł Prałat, Gregory B. Sorkin
1 citations
Random Structures and Algorithms
Limits and Structures in Graph Theory
5.33
article

Perfect Matchings and Loose Hamilton Cycles in the Semirandom Hypergraph Model

Michael Molloy, Paweł Prałat, Gregory B. Sorkin
article en
1 citations

Abstract

ABSTRACT We study the 2‐offer semirandom 3‐uniform hypergraph model on vertices. At each step, we are presented with 2 uniformly random vertices. We choose any other vertex, thus creating a hyperedge of size 3. We show a strategy that constructs a perfect matching and another that constructs a loose Hamilton cycle, both succeeding asymptotically almost surely within steps. Both results extend to ‐uniform hypergraphs. Our methods are qualitatively different from those that have been used for semirandom graphs. Much of the analysis is done on an auxiliary graph that is a uniform ‐out subgraph of a random bipartite graph, and this tool may be useful in other contexts.

Random Structures and AlgorithmsVol. 66(2)
University of Toronto (CA), Toronto Metropolitan University (CA), London School of Economics and Political Science (GB)
Natural Sciences and Engineering Research Council of Canada
Openalex Percentile: Top 10%
Limits and Structures in Graph Theory
5.33
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