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A simple algorithm for graph reconstruction

Authors: Mathieu, Claire; Zhou, Hang;

A simple algorithm for graph reconstruction

Abstract

AbstractHow 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 distance queries. In our work, we analyze a simple reconstruction algorithm. We show that, on random ‐regular graphs, our algorithm uses distance queries. As by‐products, with high probability, we can reconstruct those graphs using queries to an all‐distances oracle or queries to a betweenness oracle, and we bound the metric dimension of those graphs by . 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.

Countries
France, Germany
Keywords

FOS: Computer and information sciences, reconstruction, Combinatorics, Computer Science - Data Structures and Algorithms, random regular graphs, Data Structures and Algorithms (cs.DS), [INFO] Computer Science [cs], Computer science, metric dimension, network topology, 004

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
Green