Local Fractional Strong Metric Dimension of Certain Complex Networks

IF 1.7 4区 工程技术 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Complexity Pub Date : 2023-05-05 DOI:10.1155/2023/3635342
Faiza Jamil, Agha Kashif, Sohail Zafar, Michael Onyango Ojiema
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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 Cn, circulant networks Cn(1,2), mobious ladder networks M2n, and generalized prism networks . In this regard, it is shown that LSFMD of Cn(n ≥ 3) and is 1 when n is even and n/n − 1 when n is odd, whereas LSFMD of M2n is 1 when n is odd and n/n − 1 when n is even. Also, LSFMD of Cn(1,2) is n/2(⌈m + 1/2⌉) where n ≥ 6 and m = ⌈n − 5/4⌉.

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一类复杂网络的局部分数强度量维数
基于距离的参数的分数变量在传感器网络、机器人导航和整数规划问题等领域都有应用。复杂网络是一种表现出显著拓扑特征的特殊网络,已成为计算机科学、生物学和数学领域的典型研究领域。由于许多现实世界的系统可以被智能地建模和表示为复杂的网络,以检查、管理和理解来自这些现实世界网络的有用信息。本文计算了一类复杂网络的局部分数阶强度量维数。复杂网络的构建块被认为是对称网络,如循环网络Cn、循环网络Cn(1,2)、两栖阶梯网络M2n和广义棱镜网络。由此可见,Cn(n≥3)和的LSFMD在n为偶数时为1,在n为奇数时为n/n−1,而M2n的LSFMD在n为奇数时为1,在n为偶数时为n/n−1。同样,Cn(1,2)的LSFMD为n/2(≤n + 1/2),其中n≥6且m =≤≤n−5/4。
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来源期刊
Complexity
Complexity 综合性期刊-数学跨学科应用
CiteScore
5.80
自引率
4.30%
发文量
595
审稿时长
>12 weeks
期刊介绍: Complexity is a cross-disciplinary journal focusing on the rapidly expanding science of complex adaptive systems. The purpose of the journal is to advance the science of complexity. Articles may deal with such methodological themes as chaos, genetic algorithms, cellular automata, neural networks, and evolutionary game theory. Papers treating applications in any area of natural science or human endeavor are welcome, and especially encouraged are papers integrating conceptual themes and applications that cross traditional disciplinary boundaries. Complexity is not meant to serve as a forum for speculation and vague analogies between words like “chaos,” “self-organization,” and “emergence” that are often used in completely different ways in science and in daily life.
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