统合模糊、中性与不确定图论，构建复杂系统建模新框架
Fuzzy, Neutrosophic, and Uncertain Graph Theory: Properties and Applications
- 提出统一的不确定图框架，整合多种不确定性图模型
- 涵盖从超图到动态图的扩展结构，支持多类复杂系统建模
- 适用于分子结构分析、决策系统与知识图谱等实际场景
本书系统综述了不确定性环境下的图论，重点突出不确定图框架的统一作用。全面梳理了模糊图、中性图及相关模型中的基本概念、结构属性、图类与图参数；引入了不确定有向图、超图、超级超图及动态图等多种扩展形式。除理论发展外，还探讨了在不确定分子图、决策系统、图神经网络、知识图谱与认知地图中的应用。通过将多样化的不确定性图模型置于统一视角下，本研究为理解其相互关系、能力与在复杂系统中的应用提供了连贯框架。
原文摘要 · Abstract (English)
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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