Sharp bound on the threshold metric dimension of trees
Zsolt Bartha, J\'ulia Komj\'athy, J\"arvi Raes

TL;DR
This paper establishes a precise linear lower bound on the threshold-$k$ metric dimension of trees, disproving previous conjectures and introducing new structural insights and constructions for optimal sensor placement.
Contribution
It provides a sharp lower bound on the threshold-$k$ metric dimension of trees depending only on size and radius, along with constructions and structural analysis.
Findings
The lower bound grows linearly with the number of vertices.
Disproves earlier conjectures about the main order term.
Introduces the concept of 'attraction of sensors' for structural analysis.
Abstract
The threshold- metric dimension () of a graph is the minimum number of sensors -- a subset of the vertex set -- needed to uniquely identify any vertex in the graph, solely based on its distances from the sensors, when the measuring radius of a sensor is . We give a sharp lower bound on the of trees, depending only on the number of vertices and the measuring radius . This sharp lower bound grows linearly in with leading coefficient , disproving earlier conjectures by Tillquist et al. in arXiv:2106.14314 that suspected as main order term. We provide a construction for the largest possible trees with a given value. The proof that our optimal construction cannot be improved relies on edge-rewiring procedures of arbitrary (suboptimal) trees with…
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Taxonomy
TopicsGraph Labeling and Dimension Problems · Graph theory and applications
