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arXiv 2512.08899math.CO
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关于稀疏随机图的普拉格维度

On the Prague dimension of sparse random graphs

Felix Joos, Letícia Mattos

AI总结:

本文研究了稀疏随机图的普拉格维度,通过分析随机贪心过程,证明其维度为Θ_ε(pn),并改进了最近的相关结果。

摘要:

图G的普拉格维度被定义为包含G作为诱导子图的最少完全图的直接乘积数。该概念于20世纪70年代由Nešetřil、Pultr和Rödl提出,受Dushnik和Miller的工作以及诱导Ramsey定理的启发。确定图的普拉格维度是一个 notoriously 难的问题。在本文中,我们证明对于所有ε>0和p满足n^{-1+ε} ≤ p ≤ n^{-ε},以高概率G_{n,p}的普拉格维度为Θ_ε(pn),这优于Molnar、Rödl、Sales和Schacht最近的结果。受Bennett和Bohman工作的启发,我们的方法集中在分析一个随机贪心过程,该过程通过迭代地从已选顶点的共同非邻居中随机选择顶点,构建一个大小为Ω(p^{-1}log pn)的独立集。利用微分方程方法,我们证明每个非边本质上等可能被该过程覆盖,这是建立我们界限的关键。

英文摘要:

The Prague dimension of a graph $G$ is defined as the minimum number of complete graphs whose direct product contains $G$ as an induced subgraph. Introduced in the 1970s by Nešetřil, Pultr, and Rödl -- and motivated by the work of Dushnik and Miller, as well as by the induced Ramsey theorem -- determining the Prague dimension of a graph is a notoriously hard problem. In this paper, we show that for all $\varepsilon > 0$ and $p$ such that $ n^{-1+\varepsilon} \le p \le n^{-\varepsilon}$, with high probability the Prague dimension of $G_{n,p}$ is $Θ_{\varepsilon}(pn)$, which improves upon a recent result by Molnar, Rödl, Sales and Schacht. Inspired by the work of Bennett and Bohman, our approach centres on analysing a random greedy process that builds an independent set of size $Ω(p^{-1}\log pn)$ by iteratively selecting vertices uniformly at random from the common non-neighbourhood of those already chosen. Using the differential equation method, we show that every non-edge is essentially equally likely to be covered by this process, which is key to establishing our bound.

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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