License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.APPROX-RANDOM.2019.66
URN: urn:nbn:de:0030-drops-112819
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2019/11281/
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Anastos, Michael ; Michaeli, Peleg ; Petti, Samantha

Thresholds in Random Motif Graphs

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Abstract

We introduce a natural generalization of the Erdös-Rényi random graph model in which random instances of a fixed motif are added independently. The binomial random motif graph G(H,n,p) is the random (multi)graph obtained by adding an instance of a fixed graph H on each of the copies of H in the complete graph on n vertices, independently with probability p. We establish that every monotone property has a threshold in this model, and determine the thresholds for connectivity, Hamiltonicity, the existence of a perfect matching, and subgraph appearance. Moreover, in the first three cases we give the analogous hitting time results; with high probability, the first graph in the random motif graph process that has minimum degree one (or two) is connected and contains a perfect matching (or Hamiltonian respectively).

BibTeX - Entry

@InProceedings{anastos_et_al:LIPIcs:2019:11281,
  author =	{Michael Anastos and Peleg Michaeli and Samantha Petti},
  title =	{{Thresholds in Random Motif Graphs}},
  booktitle =	{Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2019)},
  pages =	{66:1--66:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-125-2},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{145},
  editor =	{Dimitris Achlioptas and L{\'a}szl{\'o} A. V{\'e}gh},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{http://drops.dagstuhl.de/opus/volltexte/2019/11281},
  URN =		{urn:nbn:de:0030-drops-112819},
  doi =		{10.4230/LIPIcs.APPROX-RANDOM.2019.66},
  annote =	{Keywords: Random graph, Connectivity, Hamiltonicty, Small subgraphs}
}

Keywords: Random graph, Connectivity, Hamiltonicty, Small subgraphs
Collection: Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (APPROX/RANDOM 2019)
Issue Date: 2019
Date of publication: 17.09.2019

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