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In this research paper, we apply the idea of bipolar neutrosophic sets to incidence graphs. We present some notions, including bipolar neutrosophic incidence graphs, bipolar neutrosophic incidence cycle and bipolar neutrosophic incidence tree. We define strong path, strength and incidence strength of strongest path in bipolar neutrosophic incidence graphs. We investigate some properties of bipolar neutrosophic incidence graphs. We also describe an application of bipolar neutrosophic incidence graphs.
In this research paper, we apply the idea of bipolar neutrosophic sets to incidence graphs. We present some notions, including bipolar neutrosophic incidence graphs, bipolar neutrosophic incidence cycle and bipolar neutrosophic incidence tree. We define strong path, strength and incidence strength of strongest path in bipolar neutrosophic incidence graphs. We investigate some properties of bipolar neutrosophic incidence graphs. We also describe an application of bipolar neutrosophic incidence graphs.
A bipolar model is a significant model wherein positive data revels the liked object, while negative data speaks the disliked object. The principle reason for analysing the vague graphs is to demonstrate the stability of few properties in a graph, characterized or to be characterized in using vagueness. In this present research article, the new concept of neutrosophic bipolar vague sets are initiated. Further, its application to neutrosophic bipolar vague graphs are introduced. Moreover, some remarkable properties of strong neutrosophic bipolar vague graphs, complete neutrosophic bipolar vague graphs and complement neutrosophic bipolar vague graphs are explored and the proposed ideas are outlined with an appropriate example.
A bipolar model is a significant model wherein positive data revels the liked object, while negative data speaks the disliked object. The principle reason for analysing the vague graphs is to demonstrate the stability of few properties in a graph, characterized or to be characterized in using vagueness.
“Neutrosophic Sets and Systems” has been created for publications on advanced studies in neutrosophy, neutrosophic set, neutrosophic logic, neutrosophic probability, neutrosophic statistics that started in 1995 and their applications in any field, such as the neutrosophic structures developed in algebra, geometry, topology, etc. Some articles from this issue: BMBJ-neutrosophic ideals in BCK/BCI-algebras, Neutrosophic General Finite Automata, Generalized Neutrosophic Exponential map, Implementation of Neutrosophic Function Memberships Using MATLAB Program.
“Neutrosophic Sets and Systems” has been created for publications on advanced studies in neutrosophy, neutrosophic set, neutrosophic logic, neutrosophic probability, neutrosophic statistics that started in 1995 and their applications in any field, such as the neutrosophic structures developed in algebra, geometry, topology, etc.
“Neutrosophic Sets and Systems” has been created for publications on advanced studies in neutrosophy, neutrosophic set, neutrosophic logic, neutrosophic probability, neutrosophic statistics that started in 1995 and their applications in any field, such as the neutrosophic structures developed in algebra, geometry, topology, etc.
“Neutrosophic Sets and Systems” has been created for publications on advanced studies in neutrosophy, neutrosophic set, neutrosophic logic, neutrosophic probability, neutrosophic statistics that started in 1995 and their applications in any field, such as the neutrosophic structures developed in algebra, geometry, topology, etc.
“Neutrosophic Sets and Systems” has been created for publications on advanced studies in neutrosophy, neutrosophic set, neutrosophic logic, neutrosophic probability, neutrosophic statistics that started in 1995 and their applications in any field, such as the neutrosophic structures developed in algebra, geometry, topology, etc
One of the important non-linear data structures in Computer Science is graph. Most of the real life network, be it a road transportation network, or airlines network or a communication network etc., cannot be exactly transformed into a graph model, but into a Multigraphs model. The Multigraph is a topological generalization of the graph where multiple links (or edges/arcs) mayexist between two nodes unlike in graph.