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arXiv 1707.02899math.CO
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关联图的度量维数

On the metric dimension of incidence graphs

Robert F. Bailey

摘要:

图$Γ$的解析集是顶点$S$的集合,选择这些顶点使得对于每个顶点$v$,从$v$到$S$的成员的距离列表唯一地指定$v$。度量维度$μ(Γ)$是$Γ$的解析集的最小大小。我们考虑两类关联图的度量维数:对称设计的关联图和对称横截设计(即对称网)的关联图。这些图分别是直径为$3$的二部距离正则图和直径为$4$的二部对极距离正则图。在每一种情况下,我们使用的概率方法的方式所使用的Babai获得的度量维度的强正则图的界限,并能够表明,$μ(Γ)=O(\sqrt{n}\log n)$(其中$n$是顶点数)。

英文摘要:

A resolving set for a graph $Γ$ is a collection of vertices $S$, chosen so that for each vertex $v$, the list of distances from $v$ to the members of $S$ uniquely specifies $v$. The metric dimension $μ(Γ)$ is the smallest size of a resolving set for $Γ$. We consider the metric dimension of two families of incidence graphs: incidence graphs of symmetric designs, and incidence graphs of symmetric transversal designs (i.e. symmetric nets). These graphs are the bipartite distance-regular graphs of diameter $3$, and the bipartite, antipodal distance-regular graphs of diameter $4$, respectively. In each case, we use the probabilistic method in the manner used by Babai to obtain bounds on the metric dimension of strongly regular graphs, and are able to show that $μ(Γ)=O(\sqrt{n}\log n)$ (where $n$ is the number of vertices).

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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