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Random Structures and Algorithms
Article . 2003 . Peer-reviewed
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Article . 2003
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Article . 2003
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Regularity properties for triple systems

Authors: Brendan Nagle; Vojtech Rödl;

Regularity properties for triple systems

Abstract

AbstractSzemerédi's Regularity Lemma proved to be a powerful tool in the area of extremal graph theory J. Komlós and M. Simonovits, Szemerédi's Regularity Lemma and its applications in graph theory, Combinatorics 2 (1996), 295–352. Many of its applications are based on the following technical fact: If G is a k‐partite graph with V(G) = ∪Vi, |Vi| = n for all i ∈ [k], and all pairs {Vi, Vj}, 1 ≤ i < j ≤ k, are ϵ‐regular of density d, then G contains dnk(1 + f(ϵ)) cliques K, where f(ϵ) → 0 as ϵ → 0. The aim of this paper is to establish the analogous statement for 3‐uniform hypergraphs. Our result, to which we refer as The Counting Lemma, together with Theorem 3.5 of P. Frankl and V. Rödl [Extremal problems on set systems, Random Structures Algorithms 20(2) (2002), 131–164, a Regularity Lemma for Hypergraphs, can be applied in various situations as Szemerédi's Regularity Lemma is for graphs. Some of these applications are discussed in previous papers, as well as in upcoming papers, of the authors and others. © 2003 Wiley Periodicals, Inc. Random Struct. Alg., 23: 264–332, 2003

Related Organizations
Keywords

Extremal problems in graph theory, hypergraph regularity lemma, Szemerédi's regularity lemma, Hypergraphs

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
23
Top 10%
Top 10%
Top 10%
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