How efficiently can we find an unknown graph using distance queries between its vertices? We assume that the unknown graph is connected, unweighted, and has bounded degree. The goal is to find every edge in the graph. This problem admits a reconstruction algorithm based on multi-phase Voronoi-cell decomposition and using $\tilde O(n^{3/2})$ distance queries. In our work, we analyze a simple reconstruction algorithm. We show that, on random $\Delta$-regular graphs, our algorithm uses $\tilde O(n)$ distance queries. As by-products, we can reconstruct those graphs using $O(\log^2 n)$ queries to an all-distances oracle or $\tilde O(n)$ queries to a betweenness oracle, and we bound the metric dimension of those graphs by $\log^2 n$. Our reconstruction algorithm has a very simple structure, and is highly parallelizable. On general graphs of bounded degree, our reconstruction algorithm has subquadratic query complexity.
翻译:我们如何有效地找到一个使用其顶端之间的距离查询的未知图? 我们假设未知图是连接的, 没有加权的, 并且有约束度 。 目标是在图形中找到每一个边缘 。 这个问题承认基于多阶段Voronoi- 细胞分解的重建算法, 并使用 $\ tilde O (n ⁇ 3/2 }) 的距离查询。 我们在工作中分析一个简单的重建算法。 我们的算法在随机 $\ Delta$- 普通图中显示, 我们的算法使用 $\ tilde O (n) 的距离查询。 作为副产品, 我们可以使用 $O (\ log_ 2 n) 来重建这些图表。 我们可以使用 $( log_ 2 n ) 查询到所有距离或 $\ tillde O (n) 查询到一个中间的距离或角。 我们将这些图的维度维度维度设置为 $\log2 n 。 我们的重建算法结构非常简单,, 并且非常相似。 在受约束度的一般图表上, 我们的重建算有次赤道复杂 。