Optimal Adjacency Labels for Subgraphs of Cartesian Products

Fuente: arXiv
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Main Authors: Esperet, Louis, Harms, Nathaniel, Zamaraev, Viktor
Format: Preprint
Published: 2022
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author Esperet, Louis
Harms, Nathaniel
Zamaraev, Viktor
author_facet Esperet, Louis
Harms, Nathaniel
Zamaraev, Viktor
contents For any hereditary graph class $F$, we construct optimal adjacency labeling schemes for the classes of subgraphs and induced subgraphs of Cartesian products of graphs in $F$. As a consequence, we show that, if $F$ admits efficient adjacency labels (or, equivalently, small induced-universal graphs) meeting the information-theoretic minimum, then the classes of subgraphs and induced subgraphs of Cartesian products of graphs in $F$ do too. Our proof uses ideas from randomized communication complexity, hashing, and additive combinatorics, and improves upon recent results of Chepoi, Labourel, and Ratel [Journal of Graph Theory, 2020].
format Preprint
id arxiv_https___arxiv_org_abs_2206_02872
institution arXiv
publishDate 2022
record_format arxiv
spellingShingle Optimal Adjacency Labels for Subgraphs of Cartesian Products
Esperet, Louis
Harms, Nathaniel
Zamaraev, Viktor
Data Structures and Algorithms
Combinatorics
05C78, 05C76, 68R10
For any hereditary graph class $F$, we construct optimal adjacency labeling schemes for the classes of subgraphs and induced subgraphs of Cartesian products of graphs in $F$. As a consequence, we show that, if $F$ admits efficient adjacency labels (or, equivalently, small induced-universal graphs) meeting the information-theoretic minimum, then the classes of subgraphs and induced subgraphs of Cartesian products of graphs in $F$ do too. Our proof uses ideas from randomized communication complexity, hashing, and additive combinatorics, and improves upon recent results of Chepoi, Labourel, and Ratel [Journal of Graph Theory, 2020].
title Optimal Adjacency Labels for Subgraphs of Cartesian Products
topic Data Structures and Algorithms
Combinatorics
05C78, 05C76, 68R10
url https://arxiv.org/abs/2206.02872