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PreprintCC BY 4.0arXiv

Approximating Distributions on Vertex Degrees in Random Hypergraphs via Stein's Method

  • Xianghe Zhu
  • Wenkai Xu
  • Gesine Reinert
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Abstract

Complex data are increasingly represented as hypergraphs. We establish bounds for approximating joint vertex-degree distributions by independent multivariate Poisson and correlated normal distributions in hypergraph models with independent hyperedges. We discuss the approximation of the distribution under different density regimes which are further supported by simulation studies.

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Cite

BibTeXRIS
BibTeX
@misc{zhu2026approximating,
  title = {{Approximating Distributions on Vertex Degrees in Random Hypergraphs via Stein's Method}},
  author = {Xianghe Zhu and Wenkai Xu and Gesine Reinert},
  howpublished = {arXiv},
  year = {2026},
  month = nov,
  doi = {10.48550/arxiv.2611.04705},
  eprint = {2611.04705},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2611.04705},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - Approximating Distributions on Vertex Degrees in Random Hypergraphs via Stein's Method
AU  - Xianghe Zhu
AU  - Wenkai Xu
AU  - Gesine Reinert
PY  - 2026
DA  - 2026/11/03
PB  - arXiv
DO  - 10.48550/arxiv.2611.04705
UR  - https://arxiv.org/abs/2611.04705
AB  - Complex data are increasingly represented as hypergraphs. We establish bounds for approximating joint vertex-degree distributions by independent multivariate Poisson and correlated normal distributions in hypergraph models with independent hyperedges. We discuss the approximation of the distribution under different density regimes which are further supported by simulation studies.
ER  -