A simple algorithm for graph reconstruction

IF 0.9 3区 数学 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Claire Mathieu, Hang Zhou
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引用次数: 7

Abstract

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 Õ(n3/2)$$ \overset{\widetilde }{O}\left({n}^{3/2}\right) $$ distance queries. In our work, we analyze a simple reconstruction algorithm. We show that, on random Δ$$ \Delta $$ ‐regular graphs, our algorithm uses Õ(n)$$ \overset{\widetilde }{O}(n) $$ distance queries. As by‐products, with high probability, we can reconstruct those graphs using log2n$$ {\log}^2n $$ queries to an all‐distances oracle or Õ(n)$$ \overset{\widetilde }{O}(n) $$ queries to a betweenness oracle, and we bound the metric dimension of those graphs by log2n$$ {\log}^2n $$ . 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.
一个简单的图重建算法
使用顶点之间的距离查询来查找未知图的效率如何?我们假设未知图是连通的、无权的、有界度的。目标是找出图中的每条边。该问题允许基于多相Voronoi - cell分解和使用Õ(n3/2) $$ \overset{\widetilde }{O}\left({n}^{3/2}\right) $$距离查询的重构算法。在我们的工作中,我们分析了一个简单的重建算法。我们表明,在随机Δ $$ \Delta $$‐正则图上,我们的算法使用Õ(n) $$ \overset{\widetilde }{O}(n) $$距离查询。作为副产物,在高概率下,我们可以使用log2n $$ {\log}^2n $$查询全距离oracle或Õ(n) $$ \overset{\widetilde }{O}(n) $$查询间性oracle来重建这些图,并通过log2n $$ {\log}^2n $$绑定这些图的度量维度。我们的重构算法结构简单,具有很高的并行性。对于一般有界度图,重构算法具有次二次查询复杂度。
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来源期刊
Random Structures & Algorithms
Random Structures & Algorithms 数学-计算机:软件工程
CiteScore
2.50
自引率
10.00%
发文量
56
审稿时长
>12 weeks
期刊介绍: It is the aim of this journal to meet two main objectives: to cover the latest research on discrete random structures, and to present applications of such research to problems in combinatorics and computer science. The goal is to provide a natural home for a significant body of current research, and a useful forum for ideas on future studies in randomness. Results concerning random graphs, hypergraphs, matroids, trees, mappings, permutations, matrices, sets and orders, as well as stochastic graph processes and networks are presented with particular emphasis on the use of probabilistic methods in combinatorics as developed by Paul Erdõs. The journal focuses on probabilistic algorithms, average case analysis of deterministic algorithms, and applications of probabilistic methods to cryptography, data structures, searching and sorting. The journal also devotes space to such areas of probability theory as percolation, random walks and combinatorial aspects of probability.
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