2020
DOI: 10.26637/mjm0s20/0001
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Narayana prime cordial labeling of generalized Petersen Graph

Abstract: Graph labelling is one of the major research area in Mathematics which was introduced by A. Rosa. A new concept of labelling namely Narayana Prime cordial labelling was introduced and studied by Murali, Thirusangu and Balamurugan. In this paper, we investigate the existence of Narayana prime cordial labelling of generalized Petersen graph.

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Cited by 2 publications
(2 citation statements)
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“…A Dynamic Survey of Graph Labeling (1) provides a comprehensive report on graph labelling. Many scholars worked on the generalised Petersen graph and shown the occurrence of many types of labelling, such as Minimum Coprime Labelings (2) , Prime Cordial Labelings (3) , Narayana Prime Cordial Labelings (4) , Difference Cordial Labelings (5) , Vertex-magic total labelings (6) , and so on. We explored the existence of Fuzzy Quotient-3 Cordial Labeling and demonstrated that the generalised Petersen graph is Fuzzy Quotient-3 Cordial.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…A Dynamic Survey of Graph Labeling (1) provides a comprehensive report on graph labelling. Many scholars worked on the generalised Petersen graph and shown the occurrence of many types of labelling, such as Minimum Coprime Labelings (2) , Prime Cordial Labelings (3) , Narayana Prime Cordial Labelings (4) , Difference Cordial Labelings (5) , Vertex-magic total labelings (6) , and so on. We explored the existence of Fuzzy Quotient-3 Cordial Labeling and demonstrated that the generalised Petersen graph is Fuzzy Quotient-3 Cordial.…”
Section: Introductionmentioning
confidence: 99%
“…We explored the existence of Fuzzy Quotient-3 Cordial Labeling and demonstrated that the generalised Petersen graph is Fuzzy Quotient-3 Cordial. we introduced the new labelling called fuzzy quotient -3 cordial labeling (4) as a function σ from the vertex set V (G) to the interval [0, 1] defined by σ (v) = r 10 , r ∈ Z 4 − {0}. Such that the induced function µ:…”
Section: Introductionmentioning
confidence: 99%