arXiv:2605.23936v1 Announce Type: cross 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 molecu
This publication represents a synthesis of ongoing research in AI and graph theory, consolidating advancements in handling uncertainty within complex data structures.
A robust theoretical framework for uncertain graph theory is crucial for developing more resilient and intelligent AI systems capable of operating in ambiguous or incomplete data environments.
This book provides foundational knowledge that could lead to new algorithms and applications for AI, especially in areas requiring decision-making under uncertainty, such as agentic systems.
- · AI researchers
- · Data scientists
- · Developers of autonomous systems
Improved theoretical understanding of graph structures in uncertain environments.
Development of more advanced AI models that can better interpret and act upon incomplete or fuzzy data.
Enhanced capabilities for AI agents to navigate and strategize within highly dynamic and unpredictable real-world scenarios.
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