Abstract

This book develops a comprehensive theoretical framework for the study of Hierarchical SuperHyperGraphs and Recursive SuperHyperGraphs, extending classical graph and hypergraph theory toward more expressive higher-order and multi-level relational structures. SuperHyperGraphs generalize hypergraphs through iterated powerset constructions, allowing vertices to be nested, set-valued, and hierarchically organized. The work introduces recursive SuperHyperGraphs by integrating n-level supervertices with recursive superhyperedges of bounded depth, where edges may contain both supervertices and lower-level recursive edges under typed incidence and well-foundedness constraints. Furthermore, the book formulates hierarchical SuperHyperGraphs as mixed-level incidence systems governed by coherence conditions to ensure structural consistency across powerset levels. Several important extensions are explored, including fuzzy, neutrosophic, uncertain, soft, rough, weighted, directed, and bidirected variants. Theoretical comparisons are also provided with related graph-theoretic models such as Meta-Graphs, Iterated Meta-Graphs, Filtrated Graphs, Iterated MultiGraphs, and Neural Graphs. Emphasizing definitions, formal properties, examples, and structural comparisons, the book establishes a foundational basis for future computational developments and interdisciplinary applications in complex systems, artificial intelligence, network science, and higher-order modeling.