Triangles in randomly perturbed graphs

IF 0.9 4区 数学 Q3 COMPUTER SCIENCE, THEORY & METHODS Combinatorics, Probability & Computing Pub Date : 2020-11-15 DOI:10.1017/S0963548322000153
Julia Böttcher, Olaf Parczyk, Amedeo Sgueglia, J. Skokan
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引用次数: 10

Abstract

We study the problem of finding pairwise vertex-disjoint triangles in the randomly perturbed graph model, which is the union of any $n$-vertex graph $G$ with linear minimum degree and the binomial random graph $G(n,p)$. We prove that asymptotically almost surely $G \cup G(n,p)$ contains $\min \{\delta(G), \lfloor n/3 \rfloor \}$ pairwise vertex-disjoint triangles, provided $p \ge C \log n/n$, where $C$ is a large enough constant. This is a perturbed version of an old result of Dirac. Our result is asymptotically optimal and answers a question of Han, Morris, and Treglown [RSA, to appear] in the case of triangle-factors. Together with a result of Balogh, Treglown, and Wagner [CPC, 2019, no. 2, 159-176] this fully resolves the existence of triangle-factors in randomly perturbed graphs. We also prove a stability version of our result. Finally, we discuss further generalisations to larger clique-factors, larger cycle-factors, and $2$-universality.
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随机摄动图中的三角形
研究了任意具有线性最小度的$n$ -顶点图$G$与二项随机图$G(n,p)$的并集的随机摄动图模型中寻找两两顶点不相交三角形的问题。我们渐近地几乎肯定地证明$G \cup G(n,p)$包含$\min \{\delta(G), \lfloor n/3 \rfloor \}$对顶点不相交三角形,假设$p \ge C \log n/n$,其中$C$是一个足够大的常数。这是狄拉克旧结果的扰动版本。我们的结果是渐近最优的,并回答了Han, Morris和Treglown [RSA,出现]在三角形因子的情况下的问题。连同Balogh, treglon和Wagner的结果[CPC, 2019, no. 5]。[2,159 -176]这完全解决了随机摄动图中三角形因子的存在性。我们还证明了结果的稳定性版本。最后,我们讨论了进一步推广到更大的团因子、更大的循环因子和$2$ -普遍性。
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来源期刊
Combinatorics, Probability & Computing
Combinatorics, Probability & Computing 数学-计算机:理论方法
CiteScore
2.40
自引率
11.10%
发文量
33
审稿时长
6-12 weeks
期刊介绍: Published bimonthly, Combinatorics, Probability & Computing is devoted to the three areas of combinatorics, probability theory and theoretical computer science. Topics covered include classical and algebraic graph theory, extremal set theory, matroid theory, probabilistic methods and random combinatorial structures; combinatorial probability and limit theorems for random combinatorial structures; the theory of algorithms (including complexity theory), randomised algorithms, probabilistic analysis of algorithms, computational learning theory and optimisation.
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