Fuzzy, Neutrosophic, and Uncertain Graph Theory: Properties and Applications

Computer Science > Artificial Intelligence [Submitted on 25 Apr 2026] Fuzzy, Neutrosophic, and Uncertain Graph Theory: Properties and Applications Takaaki Fujita, Florentin Smarandache 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. Comments: 326 pages. Publisher: Neutrosophic Science International Association (NSIA) Publishing House. ISBN: 978-197250204-4 Subjects: Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2605.23936 [cs.AI]   (or arXiv:2605.23936v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2605.23936 Focus to learn more Submission history From: Takaaki Fujita [view email] [v1] Sat, 25 Apr 2026 07:35:19 UTC (2,032 KB) Access Paper: view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-05 Change to browse by: cs cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)