Fuzzy, Neutrosophic, and Uncertain Graph Theory: Properties and Applications
Quick Take
This book surveys uncertain graph theory, emphasizing fuzzy and neutrosophic models and their applications in areas like decision-making and graph neural networks. It introduces various extensions, including uncertain digraphs and dynamic graphs, providing a framework for understanding their relationships and capabilities in complex systems.
Key Points
- Covers fundamental concepts and structural properties of uncertain graph theory.
- Introduces extensions like uncertain digraphs, hypergraphs, and dynamic graphs.
- Explores applications in molecular graphs and cognitive maps.
- Provides a coherent framework for diverse uncertainty-aware graph models.
- Highlights the role of uncertain graphs in complex systems.
Article Excerpt
From source RSS / original summaryarXiv:2605. 23936v1 Announce Type: new Abstract: This book presents a comprehensive and systematic survey of graph theory under uncertainty, with particular emphasis on the unifying role of the uncertain graph framework. It reviews fundamental concepts, structural properties, graph classes, and graph parameters within fuzzy, neutrosophic, and related models, while also introducing a wide range of extensions such as uncertain digraphs, hypergraphs, superhypergraphs, and dynamic graphs.
In addition to theoretical developments, the book explores practical applications, including uncertain molecular graphs, decision-making systems, graph neural networks, knowledge graphs, and cognitive maps. By organizing diverse uncertainty-aware graph models within a common perspective, this work provides a coherent framework for understanding their relationships, capabilities, and applications in complex systems.
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