New Perspectives on Classical Meanness of Some Ladder Graphs

Alanazi, Abdulaziz M.; Muhiuddin, G.; Kannan, A. Rajesh; Govindan, Vediyappan

doi:10.1155/2021/9926350

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…Te super fbonacci graceful labelings of several graphs were described by Vaidya and Prajapati [24]. Muhiuddin et al defned classical mean graph labeling [25], whereas Alanazi et al expanded it to specifc ladder graphs [26]. Recently, the authors explained that the classical mean labeling of various graphs has been developed by duplicating operations [27].…”

Section: Literature Surveymentioning

confidence: 99%

Numerical and Scientific Investigation of Some Molecular Structures Based on the Criterion of Super Classical Average Assignments

Rajesh Kannan,

Alessa,

Loganathan

et al. 2024

Journal of Mathematics

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Numbering a graph is a very practical and effective technique in science and engineering. Numerous graph assignment techniques, including distance-based labeling, topological indices, and spectral graph theory, can be used to investigate molecule structures. Consider the graph G, with the injection Ω from the node set to 1,2,…,∆, where ∆ is the sum of the number of nodes and links. Assume that the induced link assignment Ω∗ is the ceiling function of the average of root square, harmonic, geometric, and arithmetic means of the vertex labels of the end vertices of each edge. If the union of range of Ω of the node set and the range of Ω of the link set is the set 1,2,…,∆, then Ω is called a super classical average assignment (SCAA). This is known as the SCAA criterion. In this study, the graphical structures corresponding to chemical structures based on the SCAA criterion are demonstrated. The graphical depiction of chemical substances was first defined and second, the union of any number of cycles Cn, the tadpole graph, the graph extracted by identifying a line of any two cycles Cm and Cn, and the graph extracted by joining any two cycles by a path are all examined in this work.

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Section: Literature Surveymentioning

confidence: 99%

Numerical and Scientific Investigation of Some Molecular Structures Based on the Criterion of Super Classical Average Assignments

Rajesh Kannan,

Alessa,

Loganathan

et al. 2024

Journal of Mathematics

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“…C exp average assignments and (1, 1, 1) face labelings for generalised prism are described in [5,6], respectively. In [7,8], Alanazi et al talked about the classical meanness of the double-sided step graph. The super (a, d)-edge-anti magic total characteristics of graphs have been highlighted by Dafik Slamin et al in [9].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Effectiveness in Various Ladder Graphs Based on the F-Centroidal Meanness Criterion

Rajesh Kannan,

Murali Krishnan,

Loganathan

et al. 2023

Mathematics

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Graph labeling allows for the representation of additional attributes or properties associated with the vertices, edges, or both of graphs. This can provide a more comprehensive and detailed representation of the system being modeled, allowing for a richer analysis and interpretation of the graph. Graph labeling in ladder graphs has a wide range of applications in engineering, computer science, physics, biology, and other fields. It can be applied to various problem domains, such as image processing, wireless sensor networks, VLSI design, bioinformatics, social network analysis, transportation networks, and many others. The versatility of ladder graphs and the ability to apply graph labeling to them make them a powerful tool for modeling and analyzing diverse systems. If a function Υ is an injective vertex assignment in {1,2,…q+1} and the inductive edge assignment function Υ* in {1,2,…q} is expressed as a graph with q edges, defined as Υ*(uv)=2[Υ(u)2+Υ(u)Υ(v)+Υ(v)2]3[Υ(u)+Υ(v)], then the function is referred to as F-centroidal mean labeling. This is known as the F-centroidal mean criterion. Here, we have determined the F-centroidal mean criteria of the graph ladder, slanting ladder, triangular ladder, TLn∘Sm, SLn∘Sm for m≤2, double-sided step ladder, Dn*, and diamond ladder.

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Assignment Computations Based on C_exp Average in Various Ladder Graphs

Kannan¹,

Manivannan²,

Loganathan

et al. 2022

Journal of Mathematics

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This study introduces the C exp average assignments and investigates its properties using various ladder graphs. The ladder graphs can be found in every communication networks. Ladder networks are increasingly being used in everyday life for monitoring and environmental applications such as domestic, military, surveillance, industrial, medical applications, and traffic management. These datasets are afflicted by the average representation of the graph structure. It aids in the visualisation and comprehension of data analysis. The C exp labeling is used in sensor networks, adhoc networks, and other applications. It also efficiently creates a communication network after using noise reduction methods to remove salt and pepper noise.

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New Perspectives on Classical Meanness of Some Ladder Graphs

Cited by 5 publications

References 17 publications

Numerical and Scientific Investigation of Some Molecular Structures Based on the Criterion of Super Classical Average Assignments

Numerical and Scientific Investigation of Some Molecular Structures Based on the Criterion of Super Classical Average Assignments

Enhanced Effectiveness in Various Ladder Graphs Based on the F-Centroidal Meanness Criterion

Assignment Computations Based on C_exp Average in Various Ladder Graphs

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New Perspectives on Classical Meanness of Some Ladder Graphs

Cited by 5 publications

References 17 publications

Numerical and Scientific Investigation of Some Molecular Structures Based on the Criterion of Super Classical Average Assignments

Numerical and Scientific Investigation of Some Molecular Structures Based on the Criterion of Super Classical Average Assignments

Enhanced Effectiveness in Various Ladder Graphs Based on the F-Centroidal Meanness Criterion

Assignment Computations Based on Cexp Average in Various Ladder Graphs

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Assignment Computations Based on C_exp Average in Various Ladder Graphs