Centrality measures in fuzzy social networks

Decision Making Based on Valued Fuzzy Superhypergraphs

“…In addition, the book also sheds light on real-world applications of these hypergraphs, making it a valuable resource for students and researchers in the field of mathematics, as well as computer and social scientists [17]. There is also some research about fuzzy (hyper) graphs and their applications in complex hypernetworks, such as the implementation of single-valued neutrosophic soft hypergraphs on the human nervous system [18], decision-making methods based on fuzzy soft competition hypergraphs [19], hypergraph and network flow-based quality function deployment [20], global domination in fuzzy graphs using strong arcs [21], fuzzy hypergraph modeling, analysis and prediction of crimes [22], single-valued neutrosophic directed (hyper) graphs and applications in networks [23], achievable single-valued neutrosophic graphs in wireless sensor networks [24], fuzzy hypergraph network for recommending top-k profitable stocks [25], an algorithm to compute the strength of competing interactions in the bearing sea based on Pythagorean fuzzy hypergraphs [26] and centrality measures in fuzzy social networks [27]. Recently, Smarandache extended hypergraphs to a new concept as nsuperhypergraph and Plithogenic n-superhypergraph which have several properties and are connected with the real-world [28].…”

Exploring the Role of Indirect Coupling in Complex Networks: The Emergence of Chaos and Entropy in Fractional Discrete Nodes

“…In graph theory, the components of a system that exchange information are called nodes, and the medium of exchange is known as link or coupling. These concepts apply to any network with two or more nodes, such as social networks [ 1 , 2 ], transportation networks [ 3 , 4 ], biological networks [ 5 , 6 ], and neural networks [ 7 ]. In a complex network, nodes can be directly coupled with each other, or they can be linked through intermediary systems [ 8 ].…”