Submitted:
26 December 2025
Posted:
29 December 2025
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Abstract
Keywords:
1. Preliminaries
1.1. SuperHyperGraphs
- V is a finite set (the vertices), and
- E is a finite family of nonempty subsets of V (the hyperedges).
1.2. Rooted Tree
2. Main Results
2.1. Depth-n Tree-Vertex Graph
- (i)
- is a rooted tree whose leaves have depth 0 and whose root has depth n. For each define the level set
- (ii)
-
η is a nested labeling (level-typed label map)satisfying:
- (a)
- (Level-typing) For every , one has
- (b)
- (Leaf grounding) The restriction is a bijection (so each leaf represents exactly one base vertex).
- (c)
-
(Recursive nesting along the tree) For every internal node with ,so the label of u is literally the set of its children’s labels (hence lives in the next iterated powerset).
- (iii)
-
The support (flattened base-vertex set) of a tree-vertex is defined bywhere is from Definition 4.
- (iv)
-
For each level , is a set of level-k graph edges :An edge encodes a binary relation within the same abstraction depth k between the hierarchical units represented by u and v.
- (i)
-
Rooted tree. Let the node set beDeclare r to be the root. Define the parent–child arcsThen the leaves are , the level-1 nodes are , and the unique level-2 node is .
- (ii)
-
Nested labeling . We specify byBy construction:
- for each ,
- for ,
- for ,
and the recursive nesting condition holds for the internal nodes. - (iii)
- Supports (flattened base-vertex sets). Using , we obtain
- (iv)
- Level edges . Define
- (i)
-
Rooted tree. Let the leaves (depth 0) beLet the level-1 nodes bethe level-2 nodes beand the root (depth 3) beSet and define arcs
- (ii)
-
Nested labeling . Define η on leaves by the bijectionThen define η on internal nodes by the recursive rule:
- (iii)
- Supports (flattened base-vertex sets). Using , we obtain
- (iv)
- Level edges . Define
2.2. n-SuperHyperGraph with Intra-Level and Inter-Level Edges
- (i)
-
(Graded supervertex set)Optionally (and typically), one assumes grounding (equivalently ), and for .
- (ii)
-
(Superhyperedges)Each is a nonempty set of supervertices, possibly mixing levels.
- (iii)
-
(Support / flattening) For define its supportwhere is the flattening map from Definition 4. (Thus is the set of base vertices “contained in” the nested object u.)
- (iv)
-
(Intra-level (graph) edges) For each ,An edge encodes a binary relation between two supervertices at the same level k.
- (v)
-
(Inter-level (directed) edges) For each pair ,An ordered pair is an inter-level edge from level k to level ℓ.
- (vi)
- (Optional support-compatibility constraints) Depending on semantics, one may require every to satisfy, for example,
2.3. Depth-n TVG with Intra-Level and Inter-Level Edges
- (i)
- is a rooted tree whose leaves have depth 0 and whose root has depth n. Let .
- (ii)
-
is a nested labeling such that:
- (a)
- (Level-typing) If , then .
- (b)
- (Leaf grounding) is a bijection.
- (c)
- (Recursive nesting) For every with ,
- (iii)
- Define the flattened support by .
- (iv)
- (Intra-level edges) For each ,
- (v)
-
(Inter-level edges / cross edges) For each pair ,An ordered pair is an inter-level edge from level k to level ℓ.
- (vi)
-
(Support-compatibility constraint, optional) One may additionally require that every inter-level edge satisfies a support constraint such as either:or the weaker overlap condition , depending on the intended semantics.
- (i)
-
Rooted tree T. Let the node set beTake r as the root and define the parent–child arcsThus
- (ii)
-
Nested labeling . Define η on the leaves byand on internal nodes by the recursive nesting rule:
- (iii)
- Flattened supports . Using , we obtain
- (iv)
- Intra-level edges. Set
- (v)
- Inter-level edges. We give cross edges at both pairs of levels:
- (vi)
- Compatibility check (overlap condition). Every listed inter-level edge satisfies : for instance, has and , so the intersection is ; similarly and overlap by construction.
- (i)
-
Rooted tree T. Let the levels beLet the arc set be
- (ii)
- Nested labeling . Define for . Then recursively:
- (iii)
- Flattened supports .
- (iv)
- Intra-level edges. We specify relations within each abstraction depth:
- (v)
-
Inter-level edges (cross edges).We include a mix of upward cross edges (support inclusion ) and downward cross edges (support inclusion ) by choosing:together with two downward edges from the top:(Equivalently, one may encode these as and with reversed orientation, depending on whether the intended semantics is “refines” or “abstracts.”)
- (vi)
- Compatibility check (inclusion/overlap). For each upward edge , we have , e.g. . For each downward edge and we have and , so in particular and .
3. Conclusions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Public Involvement Statement
Use of Artificial Intelligence
Acknowledgments
Conflicts of Interest
References
- Diestel, R. Graph theory; Springer (print edition); Reinhard Diestel (eBooks), 2024. [Google Scholar]
- Gao, Y.; Zhang, Z.; Lin, H.; Zhao, X.; Du, S.; Zou, C. Hypergraph learning: Methods and practices. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020, 44, 2548–2566. [Google Scholar] [CrossRef] [PubMed]
- Feng, Y.; You, H.; Zhang, Z.; Ji, R.; Gao, Y. Hypergraph neural networks. In Proceedings of the Proceedings of the AAAI conference on artificial intelligence, 2019; pp. 3558–3565. [Google Scholar]
- Chen, J.; Schwaller, P. Molecular hypergraph neural networks. The Journal of Chemical Physics 2024, 160. [Google Scholar] [CrossRef] [PubMed]
- Ji, S.; Feng, Y.; Di, D.; Ying, S.; Gao, Y. Mode Hypergraph Neural Network. IEEE Transactions on Neural Networks and Learning Systems 2025. [Google Scholar]
- Casetti, N.; Nevatia, P.; Chen, J.; Schwaller, P.; Coley, C.W. Comment on “Molecular hypergraph neural networks”[J. Chem. Phys. 160, 144307 (2024)]. The Journal of Chemical Physics 2024, 161. [Google Scholar] [CrossRef]
- Du, W.; Zhang, S.; Cai, Z.; Li, X.; Liu, Z.; Fang, J.; Wang, J.; Wang, X.; Wang, Y. Molecular Merged Hypergraph Neural Network for Explainable Solvation Gibbs Free Energy Prediction. Research 2025, 8, 0740. [Google Scholar] [CrossRef]
- Bravo, J.C.M.; Piedrahita, C.J.B.; Bravo, M.A.M.; Pilacuan-Bonete, L.M. Integrating SMED and Industry 4.0 to optimize processes with plithogenic n-SuperHyperGraphs. Neutrosophic Sets and Systems 2025, 84, 328–340. [Google Scholar]
- Fujita, T. Multi-SuperHyperGraph Neural Networks: A Generalization of Multi-HyperGraph Neural Networks. Neutrosophic Computing and Machine Learning 2025, 39, 328–347. [Google Scholar]
- Marcos, B.V.S.; Willner, M.F.; Rosa, B.V.C.; Yissel, F.F.R.M.; Roberto, E.R.; Puma, L.D.B.; Fernández, D.M.M. Using plithogenic n-SuperHyperGraphs to assess the degree of relationship between information skills and digital competencies. Neutrosophic Sets and Systems 2025, 84, 513–524. [Google Scholar]
- Amable, N.H.; De Salazar, E.E.V.; Isaac, M.G.M.; Sánchez, O.C.O.; Palma, J.M.S. Representation of motivational dynamics in school environments through Plithogenic n-SuperHyperGraphs with family participation. Neutrosophic Sets and Systems 2025, 92, 570–583. [Google Scholar]
- Berrocal Villegas, S.M.; Montalvo Fritas, W.; Berrocal Villegas, C.R.; Flores Fuentes Rivera, M.Y.; Espejo Rivera, R.; Bautista Puma, L.D.; Macazana Fernández, D.M. Using plithogenic n-SuperHyperGraphs to assess the degree of relationship between information skills and digital competencies. Neutrosophic Sets and Systems 2025, 84, 41. [Google Scholar]
- Roshdy, E.; Khashaba, M.; Ali, M.E.A. Neutrosophic super-hypergraph fusion for proactive cyberattack countermeasures: A soft computing framework. Neutrosophic Sets and Systems 2025, 94, 232–252. [Google Scholar]
- Fujita, T.; Mehmood, A. SuperHyperGraph Attention Networks. Neutrosophic Computing and Machine Learning 2025, 40, 10–27. [Google Scholar]
- Fujita, T.; Smarandache, F. Superhypergraph Neural Networks and Plithogenic Graph Neural Networks: Theoretical Foundations. Advancing Uncertain Combinatorics through Graphization, Hyperization, and Uncertainization: Fuzzy, Neutrosophic, Soft, Rough, and Beyond 2025, 5, 577. [Google Scholar]
- Hamidi, M.; Smarandache, F.; Davneshvar, E. Spectrum of superhypergraphs via flows. Journal of Mathematics 2022, 2022, 9158912. [Google Scholar] [CrossRef]
- Fujita, T.; Smarandache, F. Soft Directed n-SuperHyperGraphs with Some Real-World Applications. European Journal of Pure and Applied Mathematics 2025, 18, 6643–6643. [Google Scholar] [CrossRef]
- Fujita, T. Directed Acyclic SuperHypergraphs (DASH): A General Framework for Hierarchical Dependency Modeling. Neutrosophic Knowledge 2025, 6, 72–86. [Google Scholar]
- Fujita, T. MetaHyperGraphs, MetaSuperHyperGraphs, and Iterated MetaGraphs: Modeling Graphs of Graphs, Hypergraphs of Hypergraphs, Superhypergraphs of Superhypergraphs, and Beyond. Current Research in Interdisciplinary Studies 2025, 4, 1–23. [Google Scholar]
- Nacaroglu, Y.; Akgunes, N.; Pak, S.; Cangul, I.N. Some graph parameters of power set graphs. Advances & Applications in Discrete Mathematics 2021, 26. [Google Scholar]
- Shalu, M.; Yamini, S.D. Counting maximal independent sets in power set graphs; Indian Institute of Information Technology Design & Manufacturing (IIITD&M): Kancheepuram, India, 2014. [Google Scholar]
- Jech, T. Set theory: The third millennium edition, revised and expanded; Springer, 2003. [Google Scholar]
- Bretto, A. Hypergraph theory. In An introduction. Mathematical Engineering; Springer: Cham, 2013; Volume 1. [Google Scholar]
- Berge, C. Hypergraphs: combinatorics of finite sets; Elsevier, 1984; Vol. 45. [Google Scholar]
- Smarandache, F. Extension of HyperGraph to n-SuperHyperGraph and to Plithogenic n-SuperHyperGraph, and Extension of HyperAlgebra to n-ary (Classical-/Neutro-/Anti-) HyperAlgebra. Infinite Study 2020. [Google Scholar]
- Zadeh, L.A. Fuzzy sets. Information and control 1965, 8, 338–353. [Google Scholar] [CrossRef]
- Wang, H.; Smarandache, F.; Zhang, Y.; Sunderraman, R. Single valued neutrosophic sets. Infinite study 2010. [Google Scholar]
- Broumi, S.; Talea, M.; Bakali, A.; Smarandache, F. Single valued neutrosophic graphs. Journal of New theory 2016, 10, 86–101. [Google Scholar]
- Smarandache, F. Plithogenic set, an extension of crisp, fuzzy, intuitionistic fuzzy, and neutrosophic sets-revisited. Infinite study 2018. [Google Scholar]
- Azeem, M.; Rashid, H.; Jamil, M.K.; Gütmen, S.; Tirkolaee, E.B. Plithogenic fuzzy graph: A study of fundamental properties and potential applications. Journal of Dynamics and Games 2024, 0–0. [Google Scholar] [CrossRef]
| Object | Vertex objects | Edge objects (what an edge can connect) |
|---|---|---|
| Graph | Atoms: V is a finite set of vertices. | Edges are pairs of vertices (undirected) or ordered pairs (directed): (undirected) or (directed). |
| Hypergraph | Atoms: V is a finite set of vertices. | Hyperedges are nonempty subsets of vertices: . An edge may connect any number of vertices. |
| n-SuperHyperGraph | Nested objects: , i.e., vertices may be sets-of-sets (iterated powersets). | Superedges are nonempty subsets of the (possibly nested) vertex set: . Thus edges connect collections of (nested) vertices. |
| Aspect | n-SuperHyperGraph | Depth-n Tree-Vertex Graph |
|---|---|---|
| Base objects | A finite base set is given, but vertices are chosen as . | A finite base set is given; leaves of T (level 0) are in bijection with . |
| Vertex representation | Vertices are uniform-depth set-objects: . | Vertices are tree-vertices , each typed by its depth k and labeled by . |
| Hierarchy mechanism | Hierarchy is implicit in the iterated powerset level n; there is no canonical parent–child structure among vertices. | Hierarchy is explicit: provides parent–child arcs, and labels satisfy . |
| Depth / levels | A single global level n for vertices (although edges are subsets of V). | Multiple levels present simultaneously; . |
| Flattened support in | Not intrinsic; one may apply a flattening map on if desired. | Built-in: each tree-vertex has support . |
| Edges | Hyperedges are nonempty subsets of V: (multiway relations between supervertices). | Level edges are binary within each level: . |
| Granularity of relations | Relations are expressed as set-valued edges (hyperedges) among set-valued vertices. | Relations are expressed as graph edges among hierarchical units at the same abstraction level. |
| Typical modeling intent | Nested (higher-order) vertex objects with hyperedges capturing multiway interactions at the chosen level. | A hierarchical decomposition of via a tree, with additional same-level relational structure among the resulting clusters at each depth. |
| Aspect | Classical n-SuperHyperGraph | n-SHG with intra-/inter-level edges (Definition 8) |
|---|---|---|
| Base set | Finite nonempty | Finite nonempty |
| Vertex objects | Single-level supervertices: | Graded supervertices: with level slices |
| Grounding at level 0 | Not required by definition (often implicit via choosing ) | Optional but typical: (i.e., base vertices appear as level-0 supervertices) |
| Primary relations | Hyperedges only: | Hyperedges plus binary edges within and across levels |
| Hyperedges mix levels? | Not applicable (only level n vertices exist) | Yes: may contain supervertices from different levels (unless restricted) |
| Intra-level edges | None | For each k, undirected graph edges |
| Inter-level edges | None | For , directed cross edges |
| Support / flattening to | Not part of the minimal definition (can be added externally) | Built-in via for , enabling semantic constraints on cross edges |
| Expressivity gain | Encodes higher-order (set-valued) vertices and hyperedges at one fixed nesting depth n | Adds explicit multi-resolution (levels 0 to n) and explicit binary relations both within a level and between different depths |
| Recovery of classical model | By definition | If one restricts to and discards and , then reduces to a classical n-SuperHyperGraph (Remark 3). |
| Aspect | Depth-n TVG (level edges only) | Depth-n TVG with intra- and inter-level edges |
|---|---|---|
| Core structure | Rooted tree of fixed depth n with level sets | Same rooted tree backbone T with the same level decomposition |
| Objects (vertices) | Tree-vertices (hierarchical units) | Same tree-vertices |
| Typing / hierarchy encoding | Nested labeling is level-typed: , and for | Same nested, level-typed labeling (hierarchy still enforced by the tree and recursion) |
| Grounding at level 0 | is a bijection | Same leaf grounding |
| Flattened support | Same support definition | |
| Intra-level edges | For each k, an undirected edge set | Same intra-level edge sets |
| Inter-level (cross) edges | Not present | Present as directed relations across levels: for |
| Typical semantics of extra edges | Relations only among units at the same abstraction depth | Relations can connect units of different abstraction depths (e.g., “refines/abstracts/depends-on/explains”) |
| Optional constraints | (Usually none beyond the tree/label recursion) | May impose support-compatibility on (e.g., , or ) |
| Aspect | Depth-n TVG with intra- and inter-level edges | n-SHG with intra- and inter-level edges |
|---|---|---|
| Underlying “hierarchy carrier” | A rooted tree T fixes the hierarchy: every non-leaf has children and the nesting is along parent–child arcs | No tree is required; hierarchy is encoded by membership in iterated powersets (i.e., by level k in ) and by chosen supervertices |
| Vertex objects | Tree-vertices (each represents a hierarchical unit) | Supervertices (chosen nested set-objects) |
| Level decomposition | Levels are with fixed depth n | Levels are (graded by iterated powerset depth) |
| How higher-level objects are formed | Rigid recursion: (every internal node is exactly the set of its children labels) | Flexible selection: a supervertex in is any element of included in V (no requirement that it equals the set of “children” of something) |
| Grounding at level 0 | is a bijection (all base vertices appear as leaves) | Typically assumes grounding ; otherwise optional in general formulations |
| Support map | for (same flattening idea, but applied directly to nested set-objects) | |
| Intra-level (graph) edges | ||
| Inter-level (directed) edges | ||
| Higher-arity relations | Not part of the core (unless separately added); relations are primarily graph-type edges | Includes hyperedges in addition to graph-type edges |
| Modeling emphasis | Tree-shaped, laminar, and recursively generated clusters; edges attach to explicitly organized hierarchical units | General nested set-valued vertices with both hyperedges (multiway) and graph-type edges (binary), without a required tree backbone |
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