Intuitionistic Fuzzy Quasi-Supergraph Integration for Social Network Decision Making

This study explores the complexities of intuitionistic fuzzy (hyper) graphs, considering them as complex (hyper) networks, and presents a unique idea for intuitionistic fuzzy (quasi) superhypergraphs. The extension considers intuitionistic fuzzy superhypergraphs to be complicated superhyper networks to establish particular and general links between labeled items. These intuitionistic fuzzy (quasi) superhypergraphs arrange labeled object groups and analyze them in several relational aspects at the same time, including part-to-part, part-to-whole, and whole-to-whole groupings. The research investigates the characteristics of intuitionistic fuzzy (quasi) superhypergraphs utilizing positive real numbers, such as valued intuitionistic fuzzy (quasi) superhypergraphs and their complements, permutation-based isomorphism notation, and isomorphic (self-complemented) valued intuitionistic fuzzy (quasi) superhypergraphs. It also presents the concept of impact membership value for intuitionistic fuzzy (quasi) superhypergraphs and demonstrates how it may be used to solve real-world problems. Finally, the research demonstrates the use of intuitionistic fuzzy valued quasi superhyper graphs in addressing social network analysis, emphasizing their practical use.