Notions of Rough Neutrosophic Digraphs

Abstract

Graph theory has numerous applications in various disciplines, including computer networks, neural networks, expert systems, cluster analysis, and image capturing. Rough neutrosophic set (NS) theory is a hybrid tool for handling uncertain information that exists in real life. In this research paper, we apply the concept of rough NS theory to graphs and present a new kind of graph structure, rough neutrosophic digraphs. We present certain operations, including lexicographic products, strong products, rejection and tensor products on rough neutrosophic digraphs. We investigate some of their properties. We also present an application of a rough neutrosophic digraph in decision-making.

Certain Concepts in Directed Rough Fuzzy Graphs and Application to Mergers of Companies

“…In 2021, Anitha et al [32] introduced metric dimensions in rough graphs along with their mathematical properties. Park et al [33] introduced soft covering based rough graphs and studied uncertainty in soft graphs. Ishfaq et al [34] introduced rough neutrosophic digraphs, in which they have approximated neutrosophic set under the influence of a crisp equivalence relation.…”

Soft Rough Neutrosophic Influence Graphs with Application

“…Therefore, it is necessary to develop hybrid models by incorporating the advantages of many other different mathematical models dealing uncertainty. Recently, new hybrid models, including rough fuzzy graphs [40,41], fuzzy rough graphs [42], intuitionistic fuzzy rough graphs [43,44], rough neutrosophic graphs [45] and neutrosophic soft rough graphs [46] are constructed. For other notations and definitions, the readers are refereed to [47][48][49][50][51].…”