arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2003.13139math.CO
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1-2-3猜想对最小度足够大的图成立

The 1-2-3 Conjecture holds for graphs with large enough minimum degree

Jakub Przybyło

摘要:

一个简单图通常包含度数相等的邻顶点。这特别适用于规则图中的所有邻居对,而在许多随机模型中可以预期很多这样的对。是否存在一个普适常数$K$,比如说$K=3$,使得人们总是可以从任何给定的至少有三个顶点的连通图中通过将其选定的边吹成至多$K$平行边来处理这样的对?这个问题最早是由Karoblski、Karuczak和Karamason在2004年提出的,他们提出了一个等价的问题,即是否可以给每个这样的图的边分配权重$1,2,3$,使得邻顶点得到不同的加权度--它们的关联权重之和。这个基本问题现在通常被称为1-2-3猜想,并且已经在多篇论文中得到了解决。到目前为止,已知权重$1,2,3,4,5$是足够的[J. Combin.理论系列B 100(2010)347-349]。证明了当图的最小度$δ$足够大时,即当图的最大度为$δ= Ω(\logΔ)$时,这个猜想成立,其中$Δ$表示图的最大度.我们的概率证明背后的原则思想依赖于关联随机变量与一个特殊的和精心设计的分布到大多数的顶点的一个给定的图,然后选择权重的主要部分的边缘取决于这些变量的值在一个确定性或随机的方式。

英文摘要:

A simple graph more often than not contains adjacent vertices with equal degrees. This in particular holds for all pairs of neighbours in regular graphs, while a lot such pairs can be expected e.g. in many random models. Is there a universal constant $K$, say $K=3$, such that one may always dispose of such pairs from any given connected graph with at least three vertices by blowing its selected edges into at most $K$ parallel edges? This question was first posed in 2004 by Karoński, Łuczak and Thomason, who equivalently asked if one may assign weights $1,2,3$ to the edges of every such graph so that adjacent vertices receive distinct weighted degrees - the sums of their incident weights. This basic problem is commonly referred to as the 1-2-3 Conjecture nowadays, and has been addressed in multiple papers. Thus far it is known that weights $1,2,3,4,5$ are sufficient [J. Combin. Theory Ser. B 100 (2010) 347-349]. We show that this conjecture holds if only the minimum degree $δ$ of a graph is large enough, i.e. when $δ= Ω(\logΔ)$, where $Δ$ denotes the maximum degree of the graph. The principle idea behind our probabilistic proof relies on associating random variables with a special and carefully designed distribution to most of the vertices of a given graph, and then choosing weights for major part of the edges depending on the values of these variables in a deterministic or random manner.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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