mlsanityData to 7 Oct 2026

Nonparametric Modeling of Higher-Order Interactions via Hypergraphons

arXiv 2105.08678 ↗First posted 18 May 2021stat.ML cs.LG cs.SIcited by 4

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Abstract (arXiv)

We study statistical and algorithmic aspects of using hypergraphons, that are limits of large hypergraphs, for modeling higher-order interactions. Although hypergraphons are extremely powerful from a modeling perspective, we consider a restricted class of Simple Lipschitz Hypergraphons (SLH), that are amenable to practically efficient estimation. We also provide rates of convergence for our estimator that are optimal for the class of SLH. Simulation results are provided to corroborate the theory.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 30 of its 79 references that are in the map. First posted on arXiv 18 May 2021.

Builds on

30 of 79 references.

References · 30

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18 more
  1. 2019Community Detection for Hypergraph Networks via Regularized Tensor Power Iterationcited by 26
  2. 2015Concentration and regularization of random graphscited by 24
  3. 2015Consistency of Spectral Hypergraph Partitioning under Planted Partition Modelcited by 23
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  9. 2015Spectral Detection on Sparse Hypergraphscited by 14
  10. 2016Uniform Hypergraph Partitioning: Provable Tensor Methods and Sampling Techniquescited by 13
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  13. 2016Graphons, mergeons, and so on!cited by 10
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Cited by year

Citing papers per year: 2021: 2; 2022: 0; 2023: 2.

4 papers.

  1. 2021Latent Space Model for Higher-order Networks and Generalized Tensor Decompositioncited by 13
  2. 2021Statistical and Computational Efficiency for Smooth Tensor Estimation with Unknown Permutationscited by 2
  3. 2023Degree Heterogeneity in Higher-Order Networks: Inference in the Hypergraph boldsymbolβ-Modelcited by 2
  4. 2023Statistical and computational rates in high rank tensor estimationcited by 0