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On the Fractional Metric Dimension of Convex Polytopes

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  • M. K. Aslam
  • Muhammad Javaid
  • Q. Zhu
  • Abdul Raheem

Abstract

In order to identify the basic structural properties of a network such as connectedness, centrality, modularity, accessibility, clustering, vulnerability, and robustness, we need distance-based parameters. A number of tools like these help computer and chemical scientists to resolve the issues of informational and chemical structures. In this way, the related branches of aforementioned sciences are also benefited with these tools as well. In this paper, we are going to study a symmetric class of networks called convex polytopes for the upper and lower bounds of fractional metric dimension (FMD), where FMD is a latest developed mathematical technique depending on the graph-theoretic parameter of distance. Apart from that, we also have improved the lower bound of FMD from unity for all the arbitrary connected networks in its general form.

Suggested Citation

  • M. K. Aslam & Muhammad Javaid & Q. Zhu & Abdul Raheem, 2021. "On the Fractional Metric Dimension of Convex Polytopes," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-13, September.
    • Handle: RePEc:hin:jnlmpe:3925925
    DOI: 10.1155/2021/3925925
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    Cited by:

    1. Arooba Fatima & Ahmed Alamer & Muhammad Javaid, 2022. "Computing Sharp Bounds of Metric Based Fractional Dimensions for the Sierpinski Networks," Mathematics, MDPI, vol. 10(22), pages 1-12, November.

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