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arXiv 2204.04479math.CO
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关于完全满 $t$ 叉树的局部反魔幻顶点着色

On local antimagic vertex coloring for complete full $t$-ary trees

Martin Bača, Andrea Semaničová-Feňovčíková, Ruei-Ting Lai, Tao-Ming Wang

AI总结:

本文验证了关于完全满 $t$ 叉树的局部反魔幻色数猜想,并证明当 $t$ 为奇数时,其精确值为叶子数加一。

摘要:

设 $G = (V, E)$ 是一个没有 $K_2$ 分量的有限简单无向图。双射 $f : E \rightarrow \{1, 2, \cdots, |E|\}$ 被称为局部反魔幻标号,如果对于任意两个相邻顶点 $u$ 和 $v$,它们的顶点和不同,即 $w(u) \neq w(v)$,其中顶点和 $w(u) = \sum_{e \in E(u)} f(e)$,而 $E(u)$ 是与 $u$ 关联的边的集合。因此,任何局部反魔幻标号都会诱导 $G$ 的一个正常顶点着色,其中顶点 $v$ 被赋予颜色(顶点和)$w(v)$。局部反魔幻色数 $\chi_{la}(G)$ 是 $G$ 的所有由局部反魔幻标号诱导的着色中所用颜色的最小数目。曾有猜想(见文献 \cite{Aru-Wang})指出,对于每棵树 $T$,局部反魔幻色数满足 $l+1 \leq \chi_{la}(T) \leq l+2$,其中 $l$ 是 $T$ 的叶子数。在本文中,我们验证了上述猜想对于完全满 $t$ 叉树($t \geq 2$)成立。完全满 $t$ 叉树是一棵有根树,其中除叶子外所有节点都恰好有 $t$ 个孩子,并且每个叶子具有相同的深度。特别地,我们得到所有完全满 $t$ 叉树的局部反魔幻色数的精确值为 $l+1$(当 $t$ 为奇数时)。

英文摘要:

Let $G = (V, E)$ be a finite simple undirected graph without $K_2$ components. A bijection $f : E \rightarrow \{1, 2,\cdots, |E|\}$ is called a local antimagic labeling if for any two adjacent vertices $u$ and $v$, they have different vertex sums, i.e., $w(u) \neq w(v)$, where the vertex sum $w(u) = \sum_{e \in E(u)} f(e)$, and $E(u)$ is the set of edges incident to $u$. Thus any local antimagic labeling induces a proper vertex coloring of $G$ where the vertex $v$ is assigned the color (vertex sum) $w(v)$. The local antimagic chromatic number $χ_{la}(G)$ is the minimum number of colors taken over all colorings induced by local antimagic labelings of $G$. It was conjectured \cite{Aru-Wang} that for every tree $T$ the local antimagic chromatic number $l+ 1 \leq χ_{la} ( T )\leq l+2$, where $l$ is the number of leaves of $T$. In this article we verify the above conjecture for complete full $t$-ary trees, for $t \geq 2$. A complete full $t$-ary tree is a rooted tree in which all nodes have exactly $t$ children except leaves and every leaf is of the same depth. In particular we obtain that the exact value for the local antimagic chromatic number of all complete full $t$-ary trees is $ l+1$ for odd $t$.

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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