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Long Monotone Trails in Random Edge-Labellings of Random Graphs

Published online by Cambridge University Press:  08 October 2019

Omer Angel*
Affiliation:
Department of Mathematics, University of British Columbia, Vancouver, BC, V6T 1Z2, Canada
Asaf Ferber
Affiliation:
Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Benny Sudakov
Affiliation:
Department of Mathematics, ETH, 8092 Zurich, Switzerland
Vincent Tassion
Affiliation:
Department of Mathematics, ETH, 8092 Zurich, Switzerland
*
*Corresponding author. Email: [email protected]

Abstract

Given a graph G and a bijection f : E(G) → {1, 2,…,e(G)}, we say that a trail/path in G is f-increasing if the labels of consecutive edges of this trail/path form an increasing sequence. More than 40 years ago Chvátal and Komlós raised the question of providing worst-case estimates of the length of the longest increasing trail/path over all edge orderings of Kn. The case of a trail was resolved by Graham and Kleitman, who proved that the answer is n-1, and the case of a path is still wide open. Recently Lavrov and Loh proposed studying the average-case version of this problem, in which the edge ordering is chosen uniformly at random. They conjectured (and Martinsson later proved) that such an ordering with high probability (w.h.p.) contains an increasing Hamilton path.

In this paper we consider the random graph G = Gn,p with an edge ordering chosen uniformly at random. In this setting we determine w.h.p. the asymptotics of the number of edges in the longest increasing trail. In particular we prove an average-case version of the result of Graham and Kleitman, showing that the random edge ordering of Kn has w.h.p. an increasing trail of length (1-o(1))en, and that this is tight. We also obtain an asymptotically tight result for the length of the longest increasing path for random Erdős-Renyi graphs with p = o(1).

MSC classification

Type
Paper
Copyright
© Cambridge University Press 2019 

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Footnotes

Supported in part by NSERC.

Research is partially supported by an NSF grant 6935855.

§

Research supported in part by SNSF grant 200021-175573.

Research supported in part by the NCCR SwissMAP, funded by the Swiss National Science Foundation.

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