Spherical fuzzy hypergraph in decision making

This study presents the Spherical Fuzzy Hypergraph (SFHG), an advancement that integrates the principles of fuzzy theory with hypergraph models, and improving decision-making processes. The SFHG extends the existing frameworks of Pythagorean fuzzy sets and Picture fuzzy sets. Unlike spherical fuzzy graphs, SFHGs are capable of representing multi-arity relations, which prove beneficial for various applications including partitioning, clustering, and covering tasks, particularly in circuit design and the simulation of complex system architectures. This paper develops the theory of SFHGs, explaining their distinctive characteristics with illustrative examples. The practical applicability of SFHGs is demonstrated through a real-world application by optimizing decision-making in e-marketing companies. The results demonstrate improvements over existing fuzzy graph methodologies.

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Publication Details

Journal
Scientific Reports
Published
2026-03-23
DOI
https://doi.org/10.1038/s41598-026-44917-3
Primary Topic
Cognitive Science and Mapping
Type
article
Field-Weighted Citation Impact
0.00
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article

Spherical fuzzy hypergraph in decision making

Antonios Kalampakas, Tarasankar Pramanik, Rupkumar Mahapatra, Leo Mršić et al.
Scientific Reports
Cognitive Science and Mapping
article

Spherical fuzzy hypergraph in decision making

Antonios Kalampakas, Tarasankar Pramanik, Rupkumar Mahapatra, Leo Mršić, Tofigh Allahviranloo, Sovan Samanta
article en

Abstract

This study presents the Spherical Fuzzy Hypergraph (SFHG), an advancement that integrates the principles of fuzzy theory with hypergraph models, and improving decision-making processes. The SFHG extends the existing frameworks of Pythagorean fuzzy sets and Picture fuzzy sets. Unlike spherical fuzzy graphs, SFHGs are capable of representing multi-arity relations, which prove beneficial for various applications including partitioning, clustering, and covering tasks, particularly in circuit design and the simulation of complex system architectures. This paper develops the theory of SFHGs, explaining their distinctive characteristics with illustrative examples. The practical applicability of SFHGs is demonstrated through a real-world application by optimizing decision-making in e-marketing companies. The results demonstrate improvements over existing fuzzy graph methodologies.

Scientific Reports
Indian Institute of Technology Kharagpur (IN), Islamic Azad University, Science and Research Branch (IR), Azerbaijan Technical University (AZ), American University of the Middle East (KW), Kanya Maha Vidyalaya (IN), Western Caspian University (AZ), Higher Education Centre Novo Mesto (SI), Algebra Bernays University (HR), Istinye University (TR)
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Openalex Percentile: Top 13%
Cognitive Science and Mapping
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