Abstract
In this paper, we apply the notion of soft rough neutrosophic sets to graph theory. We develop certain new concepts, including soft rough neutrosophic graphs, soft rough neutrosophic influence graphs, soft rough neutrosophic influence cycles and soft rough neutrosophic influence trees. We illustrate these concepts with examples, and investigate some of their properties. We solve the decision-making problem by using our proposed algorithm.
Highlights
Smarandache [1] introduced neutrosophic sets as a generalization of fuzzy sets and intuitionistic fuzzy sets
Meng et al [19] dealt with soft rough fuzzy sets and soft fuzzy rough sets
We develop certain new concepts, including soft rough neutrosophic graphs, soft rough neutrosophic influence graphs, soft rough neutrosophic influence cycles and soft rough neutrosophic influence trees
Summary
Smarandache [1] introduced neutrosophic sets as a generalization of fuzzy sets and intuitionistic fuzzy sets. Pawlak [8] proposed the notion of rough sets It is a rigid appearance of modeling and processing partial information. We apply the notion of soft rough neutrosophic sets to graph theory. We develop certain new concepts, including soft rough neutrosophic graphs, soft rough neutrosophic influence graphs, soft rough neutrosophic influence cycles and soft rough neutrosophic influence trees. We illustrate these concepts with examples, and investigate some of their properties.
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