Metric dimensions of generalized Sierpiński graphs over squares

IF 3.5 2区 数学 Q1 MATHEMATICS, APPLIED
S. Prabhu , T. Jenifer Janany , Sandi Klavžar
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引用次数: 0

Abstract

Metric dimension is a valuable parameter that helps address problems related to network design, localization, and information retrieval by identifying the minimum number of landmarks required to uniquely determine distances between vertices in a graph. Generalized Sierpiński graphs represent a captivating class of fractal-inspired networks that have gained prominence in various scientific disciplines and practical applications. Their fractal nature has also found relevance in antenna design, image compression, and the study of porous materials. The hypercube is a prevalent interconnection network architecture known for its symmetry, vertex transitivity, regularity, recursive structure, high connectedness, and simple routing. Various variations of hypercubes have emerged in literature to meet the demands of practical applications. Sometimes, they are the spanning subgraphs of it. This study examines the generalized Sierpiński graphs over C4, which are spanning subgraphs of hypercubes and determines the metric dimension and their variants. This is in contrast to hypercubes, where these properties are inherently complicated. Along the way, the role of twin vertices in the theory of metric dimensions is further elaborated.
广义Sierpiński平方图的度量维数
度量维度是一个有价值的参数,它通过确定图中唯一确定顶点之间距离所需的最小标志数,帮助解决与网络设计、定位和信息检索相关的问题。广义Sierpiński图代表了一类迷人的分形启发网络,在各种科学学科和实际应用中获得了突出的地位。它们的分形性质也与天线设计、图像压缩和多孔材料的研究有关。超立方体是一种流行的互连网络体系结构,以其对称性、顶点传递性、规律性、递归结构、高连通性和简单路由而闻名。为了满足实际应用的需要,文献中出现了各种超立方体的变体。有时候,它们是它的生成子图。本文研究了C4上的广义Sierpiński图,这些图是超立方体的生成子图,并确定了度量维度及其变体。这与超立方体形成对比,超立方体的这些属性本质上是复杂的。同时,进一步阐述了双顶点在度量维数理论中的作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
7.90
自引率
10.00%
发文量
755
审稿时长
36 days
期刊介绍: Applied Mathematics and Computation addresses work at the interface between applied mathematics, numerical computation, and applications of systems – oriented ideas to the physical, biological, social, and behavioral sciences, and emphasizes papers of a computational nature focusing on new algorithms, their analysis and numerical results. In addition to presenting research papers, Applied Mathematics and Computation publishes review articles and single–topics issues.
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