Local Fractional Strong Metric Dimension of Certain Complex Networks

Abstract

Fractional variants of distance-based parameters have application in the fields of sensor networking, robot navigation, and integer programming problems. Complex networks are exceptional networks which exhibit significant topological features and have become quintessential research area in the field of computer science, biology, and mathematics. Owing to the possibility that many real-world systems can be intelligently modeled and represented as complex networks to examine, administer and comprehend the useful information from these real-world networks. In this paper, local fractional strong metric dimension of certain complex networks is computed. Building blocks of complex networks are considered as the symmetric networks such as cyclic networks , circulant networks , mobious ladder networks , and generalized prism networks . In this regard, it is shown that LSFMD of and is 1 when is even and when is odd, whereas LSFMD of is 1 when is odd and when is even. Also, LSFMD of is where and .

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APA

Kashi, J (2024). Local Fractional Strong Metric Dimension of Certain Complex Networks. Afribary. Retrieved from https://afribary.com/works/local-fractional-strong-metric-dimension-of-certain-complex-networks

MLA 8th

Kashi, Jamil "Local Fractional Strong Metric Dimension of Certain Complex Networks" Afribary. Afribary, 04 Jun. 2024, https://afribary.com/works/local-fractional-strong-metric-dimension-of-certain-complex-networks. Accessed 22 Dec. 2024.

MLA7

Kashi, Jamil . "Local Fractional Strong Metric Dimension of Certain Complex Networks". Afribary, Afribary, 04 Jun. 2024. Web. 22 Dec. 2024. < https://afribary.com/works/local-fractional-strong-metric-dimension-of-certain-complex-networks >.

Chicago

Kashi, Jamil . "Local Fractional Strong Metric Dimension of Certain Complex Networks" Afribary (2024). Accessed December 22, 2024. https://afribary.com/works/local-fractional-strong-metric-dimension-of-certain-complex-networks

Local Fractional Strong Metric Dimension of Certain Complex Networks

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