Pith. sign in

REVIEW 1 cited by

Chemically inspired Erd\H{o}s-R\'enyi oriented hypergraphs

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2309.06351 v1 pith:3IB763PY submitted 2023-09-12 cs.DM math.CO

classification cs.DMmath.CO
keywords orientedhypergraphsrandomnumberstructuresbeenenyiexpected
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

High-order structures have been recognised as suitable models for systems going beyond the binary relationships for which graph models are appropriate. Despite their importance and surge in research on these structures, their random cases have been only recently become subjects of interest. One of these high-order structures is the oriented hypergraph, which relates couples of subsets of an arbitrary number of vertices. Here we develop the Erd\H{o}s-R\'enyi model for oriented hypergraphs, which corresponds to the random realisation of oriented hyperedges of the complete oriented hypergraph. A particular feature of random oriented hypergraphs is that the ratio between their expected number of oriented hyperedges and their expected degree or size is 3/2 for large number of vertices. We highlight the suitability of oriented hypergraphs for modelling large collections of chemical reactions and the importance of random oriented hypergraphs to analyse the unfolding of chemistry.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Enhancing the Utility of Higher-Order Information in Relational Learning

    cs.LG 2025-02 conditional novelty 6.0 of 10

    Graph-level GNNs with new hypergraph-based encodings beat hypergraph-specific GNNs on several benchmarks, and the encodings provably increase expressivity beyond graph-level encodings.

Pith tools