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arXiv 2607.04532math.CO
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嵌入诱导有界度图

Embedding Induced Bounded Degree Graphs

Trinity College Cambridge(剑桥大学三一学院);Department of Pure Mathematics and Mathematical Statistics, Centre for Mathematical Sciences(数学科学中心纯数学与数学统计系)

Gaia Carenini

AI总结:

证明有界度图诱导嵌入的稀疏嵌入定理,该定理用于特定图对。通过结合此定理与稀疏随机转移论证,改进了有界度诱导拉姆齐数多项式界的指数。

摘要:

我们证明了有界度图诱导嵌入的稀疏嵌入定理。该定理适用于图对\(G\subseteq \Gamma\),\(G\)提供目标图的正边,\(\Gamma\)提供诱导约束。其主要特征是在整个嵌入过程中这两种约束保持分离。作为应用,改进了有界度诱导拉姆齐数多项式界的指数。证明结合了嵌入定理与稀疏随机转移论证。

英文摘要:

We prove a sparse embedding theorem for induced embeddings of bounded-degree graphs. The theorem applies to pairs $G\subseteq Γ$: the graph $G$ supplies the positive edges of the target graph, while the ambient graph $Γ$ supplies the induced constraints which must be avoided. Its main feature is that these two types of constraints are kept separate throughout the embedding process. As an application, we show that, for every fixed $Δ,r\ge2$, there are constants $A,C>0$ such that every $n$-vertex graph $H$ with maximum degree at most $Δ$ satisfies $r_{\text{ind}}(H;r)\le C n^{Δ+2}(\log n)^A$. This improves the exponent in the polynomial bound of Conlon, Fox and Zhao for bounded-degree induced Ramsey numbers. The proof combines the aforementioned embedding theorem with a sparse random transference argument, in which the random host is used only to certify robust deterministic hypotheses for every colour class.

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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