Fault-Tolerant Metric Dimension in Carbon Networks

In this paper, we study the fault-tolerant metric dimension in graph theory, an important measure against failures in unique vertex identification. The metric dimension of a graph is the smallest number of vertices required to uniquely identify every other vertex based on their distances from these chosen vertices. Building on existing work, we explore fault tolerance by considering the minimal number of vertices needed to ensure that all other vertices remain uniquely identifiable even if a specified number of these vertices fails. We compute the fault-tolerant metric dimension of various chemical graphs, namely fullerenes, benzene, and polyphenyl graphs.

Authors

Institutions

Publication Details

Journal
Foundations
Published
2025-04-16
DOI
https://doi.org/10.3390/foundations5020013
Citations
3
Primary Topic
Graph Labeling and Dimension Problems
Type
article
Field-Weighted Citation Impact
4.51
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Fault-Tolerant Metric Dimension in Carbon Networks

Asım Nadeem, Kamran Azhar, Yilun Shang
3 citations
Foundations
Graph Labeling and Dimension Problems
4.51
article

Fault-Tolerant Metric Dimension in Carbon Networks

Asım Nadeem, Kamran Azhar, Yilun Shang
article en
3 citations

Abstract

In this paper, we study the fault-tolerant metric dimension in graph theory, an important measure against failures in unique vertex identification. The metric dimension of a graph is the smallest number of vertices required to uniquely identify every other vertex based on their distances from these chosen vertices. Building on existing work, we explore fault tolerance by considering the minimal number of vertices needed to ensure that all other vertices remain uniquely identifiable even if a specified number of these vertices fails. We compute the fault-tolerant metric dimension of various chemical graphs, namely fullerenes, benzene, and polyphenyl graphs.

FoundationsVol. 5(2)
Forman Christian College (PK), Northumbria University (GB)
Openalex Percentile: Top 6%
Graph Labeling and Dimension Problems
4.51
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.