General, General Weak, Anti, Balanced, and Semi-Neutrosophic Graph

Graph classes categorize graphs based on shared properties or structures, and numerous such classes have been proposed over time. In 1965, Zadeh [43] introduced a framework for managing uncertainty, which later inspired Rosenfeld [28, 31] to develop fuzzy graph theory in 1975. A Neutrosophic Graph,...

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Main Authors: Takaaki Fujita, Florentin Smarandache
Format: Article
Language:English
Published: University of New Mexico 2025-06-01
Series:Neutrosophic Sets and Systems
Subjects:
Online Access:https://fs.unm.edu/NSS/23AntiBalanced.pdf
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author Takaaki Fujita
Florentin Smarandache
author_facet Takaaki Fujita
Florentin Smarandache
author_sort Takaaki Fujita
collection DOAJ
description Graph classes categorize graphs based on shared properties or structures, and numerous such classes have been proposed over time. In 1965, Zadeh [43] introduced a framework for managing uncertainty, which later inspired Rosenfeld [28, 31] to develop fuzzy graph theory in 1975. A Neutrosophic Graph, as a generalization of a Fuzzy Graph, associates each vertex and edge with three membership values representing truth, indeterminacy, and falsity. This allows Neutrosophic Graphs to handle uncertain, vague, or contradictory information more effectively. This paper aims to expand the study of Neutrosophic Graphs by proposing new graph classes, specifically the General-Neutrosophic Graph, Anti-Neutrosophic Graph, *-Balanced-Neutrosophic Graph, and Semi-Neutrosophic Graph.
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2331-608X
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publishDate 2025-06-01
publisher University of New Mexico
record_format Article
series Neutrosophic Sets and Systems
spelling doaj-art-4d1e40006ed84a5688220529f688b8ed2025-08-20T16:37:15ZengUniversity of New MexicoNeutrosophic Sets and Systems2331-60552331-608X2025-06-018539843510.5281/zenodo.15294849General, General Weak, Anti, Balanced, and Semi-Neutrosophic GraphTakaaki Fujita Florentin SmarandacheGraph classes categorize graphs based on shared properties or structures, and numerous such classes have been proposed over time. In 1965, Zadeh [43] introduced a framework for managing uncertainty, which later inspired Rosenfeld [28, 31] to develop fuzzy graph theory in 1975. A Neutrosophic Graph, as a generalization of a Fuzzy Graph, associates each vertex and edge with three membership values representing truth, indeterminacy, and falsity. This allows Neutrosophic Graphs to handle uncertain, vague, or contradictory information more effectively. This paper aims to expand the study of Neutrosophic Graphs by proposing new graph classes, specifically the General-Neutrosophic Graph, Anti-Neutrosophic Graph, *-Balanced-Neutrosophic Graph, and Semi-Neutrosophic Graph.https://fs.unm.edu/NSS/23AntiBalanced.pdfneutrosophic graphbalanced-neutrosophic graphgeneral-neutrosophic graphantineutrosophic graphsemi-neutrosophic graph
spellingShingle Takaaki Fujita
Florentin Smarandache
General, General Weak, Anti, Balanced, and Semi-Neutrosophic Graph
Neutrosophic Sets and Systems
neutrosophic graph
balanced-neutrosophic graph
general-neutrosophic graph
antineutrosophic graph
semi-neutrosophic graph
title General, General Weak, Anti, Balanced, and Semi-Neutrosophic Graph
title_full General, General Weak, Anti, Balanced, and Semi-Neutrosophic Graph
title_fullStr General, General Weak, Anti, Balanced, and Semi-Neutrosophic Graph
title_full_unstemmed General, General Weak, Anti, Balanced, and Semi-Neutrosophic Graph
title_short General, General Weak, Anti, Balanced, and Semi-Neutrosophic Graph
title_sort general general weak anti balanced and semi neutrosophic graph
topic neutrosophic graph
balanced-neutrosophic graph
general-neutrosophic graph
antineutrosophic graph
semi-neutrosophic graph
url https://fs.unm.edu/NSS/23AntiBalanced.pdf
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