Metric Dimension
Richard C. Tillquist, Rafael M. Frongillo, Manuel E. Lladser
TL;DR
This paper reviews the concept of metric dimension in graphs, discussing algorithms for approximation and potential applications, providing a comprehensive overview of both deterministic and random graph cases.
Contribution
It offers a concise review of metric dimension, including algorithms and applications, covering both deterministic and random graphs, which was not comprehensively compiled before.
Findings
Summarizes existing algorithms for metric dimension approximation
Highlights potential applications in network analysis
Provides insights into metric dimension in random graphs
Abstract
In this manuscript, we provide a concise review of the concept of metric dimension for both deterministic as well as random graphs. Algorithms to approximate this quantity, as well as potential applications, are also reviewed. This work has been partially funded by the NSF IIS grant 1836914.
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