Iterated Hypernode Model, Iterated UberGraph, and Iterated Relational MetaHyperGraph

The Hypernode Model and UberGraph represent nested vertices and recursive hyperedge membership, respectively, while Meta-HyperGraphs describe relations among hypergraphs. We study three finite, typed extensions of these models. An Iterated Hypernode Model is equipped with explicit cross-level links between vertex occurrences; an Iterated UberGraph treats an entire UberGraph as a fundamental vertex at the next outer level; and an Iterated Relational MetaHyperGraph uses an edge-witnessed lifting of admissible relations. We establish well-definedness, canonical injective representations, and basic properties of nesting depth and relation lifting. Examples illustrate the constructions and clarify the distinction between an incidence-preserving embedding and a recoverable higher-level encoding.

Authors

Publication Details

Journal
Preprints.org
Published
2026-11-02
DOI
https://doi.org/10.20944/preprints202611.0021.v1
Primary Topic
Advanced Graph Theory Research
Type
preprint
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
preprint

Iterated Hypernode Model, Iterated UberGraph, and Iterated Relational MetaHyperGraph

Takaaki Fujita
1 links
Preprints.org
Advanced Graph Theory Research
preprint

Iterated Hypernode Model, Iterated UberGraph, and Iterated Relational MetaHyperGraph

Takaaki Fujita
preprint en

Abstract

The Hypernode Model and UberGraph represent nested vertices and recursive hyperedge membership, respectively, while Meta-HyperGraphs describe relations among hypergraphs. We study three finite, typed extensions of these models. An Iterated Hypernode Model is equipped with explicit cross-level links between vertex occurrences; an Iterated UberGraph treats an entire UberGraph as a fundamental vertex at the next outer level; and an Iterated Relational MetaHyperGraph uses an edge-witnessed lifting of admissible relations. We establish well-definedness, canonical injective representations, and basic properties of nesting depth and relation lifting. Examples illustrate the constructions and clarify the distinction between an incidence-preserving embedding and a recoverable higher-level encoding.

Preprints.org
Advanced Graph Theory Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon