AI 中文总结
研究有限简单图中具有规定度奇偶性的诱导子图，通过定义加权计数多项式并证明等式，结合相关估计和优化得出\\(f_{\rm oe}(G)>c_*n>\frac{2n}{21}\)，改进了已有关于诱导子图阶数下界的结果。
AI 中文摘要
设\\(G=(V,E)\\)是阶数\\(n\geq1\\)的有限简单图，\\(\ell:V\to\{0,1\}\)是规定的奇偶性标记。若对于每个\\(v\in S\\)，有\\(d_S(v)\equiv\ell(v)\pmod2\\)（其中\\(d_S(v)=|N_G(v)\cap S|\)），则集合\\(S\subseteq V\)称为\\(\ell\\)-可允许的。设\\(h_\ell(G)\)是\\(\ell\\)-可允许集的最大阶数，\\(f_{\rm oe}(G)=\min_\ell h_\ell(G)\)。定义加权计数多项式\\(M_{\ell,x}(G)=\sum_{S\in {\cal A}_\ell(G)}x^{|S|}\)。证明了精确等式\\(M_{\ell,x}(G) =2^{-n}\sum_{R\subseteq V} x^{|R|}(2+x)^{z_\ell(R)}(2-x)^{n-z_\ell(R)-|R|}\)。若\\(G\)无孤立顶点，对于每个\\(\ell\)和\\(x\in(0,2)\)，有\\(M_{\ell,x}(G)>x^{n/2}(4-x^2)^{n/4}\)。结合估计与二元熵上界并优化\\(x\)可得\\(f_{\rm oe}(G)>c_*n>\frac{2n}{21}\)，改进了Ferber和Krivelevich的结果。
英文摘要
Let $G=(V,E)$ be a finite simple graph of order $n\geq 1$, and let $\ell:V\to\{0,1\}$ be a prescribed parity labeling. A set $S\subseteq V$ is called $\ell$-admissible if $d_S(v)\equiv \ell(v)\pmod 2$ for every $v\in S$, where $d_S(v)=|N_G(v)\cap S|$. Let $h_\ell(G)$ be the maximum order of an $\ell$-admissible set and let $f_{\rm oe}(G)=\min_\ell h_\ell(G)$. For $x\in\mathbb R$, define the weighted counting polynomial $$ M_{\ell,x}(G)=\sum_{S\in {\cal A}_\ell(G)}x^{|S|}, $$ where ${\cal A}_\ell(G)$ is the collection of all $\ell$-admissible sets in $G$. For $R\subseteq V$, let $z_\ell(R)$ be the number of vertices $v\in V\setminus R$ for which $d_R(v)\equiv\ell(v)\pmod 2$. We prove the exact identity $$ M_{\ell,x}(G) =2^{-n}\sum_{R\subseteq V} x^{|R|}(2+x)^{z_\ell(R)}(2-x)^{n-z_\ell(R)-|R|}. $$ If $G$ has no isolated vertices, then, for every $\ell$ and every $x\in(0,2)$, $ M_{\ell,x}(G)>x^{n/2}(4-x^2)^{n/4}. $ Combining this estimate with a binary-entropy upper bound and optimizing $x$ gives $$ f_{\rm oe}(G)>c_*n>\frac{2n}{21}, $$ where $c_*\approx0.095862615$. Ferber and Krivelevich (Adv. Math. 2022) proved that $h_{\mathbf{1}}(G)\ge 10^{-4}n$, where $\mathbf{1}$ is the all-one labeling. Since $h_{\mathbf{1}}(G)\ge f_{\rm oe}(G)$, our result improves coefficient in their bound by almost three orders of magnitude, and does so simultaneously for every labeling.
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