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https://doi.org/10.31224/5412...
Article . 2025 . Peer-reviewed
License: CC BY
Data sources: Crossref
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ZENODO
Article . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Article . 2025
License: CC BY
Data sources: Datacite
ZENODO
Article . 2025
License: CC BY
Data sources: Datacite
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Fuzzy Tolerance Hypergraphs and Fuzzy Tolerance Superhypergraphs

Authors: Fujita, Takaaki;

Fuzzy Tolerance Hypergraphs and Fuzzy Tolerance Superhypergraphs

Abstract

Graph theory offers a rigorous framework for modeling relationships and connectivity via vertices and edges [1, 2]. Hypergraphs generalize this framework by allowing hyperedges that join more than two vertices [3, 4]. Superhypergraphs further enrich the model through iterated powerset constructions, capturing hierarchical and self-referential structures among hyperedges [5]. In this paper, we introduce new classes of graphs, namely the Tolerance SuperHyperGraph, Tolerance HyperGraph, Fuzzy Tolerance Hypergraph, and Fuzzy Tolerance SuperHypergraph, and examine their properties.

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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
hybrid