Fuzzy, Neutrosophic, and Uncertain Graph Theory: Properties and Applications
Fuente:
arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866910249684828160 |
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| author | Fujita, Takaaki Smarandache, Florentin |
| author_facet | Fujita, Takaaki Smarandache, Florentin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_23936 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Fuzzy, Neutrosophic, and Uncertain Graph Theory: Properties and Applications Fujita, Takaaki Smarandache, Florentin Artificial Intelligence Machine Learning 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. |
| title | Fuzzy, Neutrosophic, and Uncertain Graph Theory: Properties and Applications |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2605.23936 |