# Core-periphery Models for Hypergraphs

arXiv: https://arxiv.org/abs/2206.00783 · HTML: https://mlsanity.com/paper/2206.00783/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 47 references that are in the map.
- First posted on arXiv 1 Jun 2022.

## Builds on (most cited first, 14 of 14)

- [Simplicial Closure and higher-order link prediction](https://mlsanity.com/paper/1802.06916/) (2018)
- [Identification of core-periphery structure in networks](https://mlsanity.com/paper/1409.4813/) (2014)
- [Three hypergraph eigenvector centralities](https://mlsanity.com/paper/1807.09644/) (2018)
- [Core-periphery detection in hypergraphs](https://mlsanity.com/paper/2202.12769/) (2022)
- [Random Spatial Network Models with Core-Periphery Structure](https://mlsanity.com/paper/1808.06544/) (2018)
- [β models for random hypergraphs with a given degree sequence](https://mlsanity.com/paper/1407.1004/) (2014)
- [Planted Hitting Set Recovery in Hypergraphs](https://mlsanity.com/paper/1905.05839/) (2019)
- [Found Graph Data and Planted Vertex Covers](https://mlsanity.com/paper/1805.01209/) (2018)
- [Core-Periphery Structure in Directed Networks](https://mlsanity.com/paper/1912.00984/) (2019)
- [Coin-flipping, ball-dropping, and grass-hopping for generating random graphs from matrices of edge probabilities](https://mlsanity.com/paper/1709.03438/) (2017)
- [The domination number of on-line social networks and random geometric graphs](https://mlsanity.com/paper/1412.1189/) (2014)
- [A Nonlinear Spectral Method for Core--Periphery Detection in Networks](https://mlsanity.com/paper/1804.09820/) (2018)
- [The HyperKron Graph Model for higher-order features](https://mlsanity.com/paper/1809.03488/) (2018)
- [Sublinear Domination and Core-Periphery Networks](https://mlsanity.com/paper/2103.03135/) (2021)

## Built on by (most cited first, 3 of 3)

- [Hypergraph Artificial Benchmark for Community Detection (h-ABCD)](https://mlsanity.com/paper/2210.15009/) (2022)
- [Broad Spectrum Structure Discovery in Large-Scale Higher-Order Networks](https://mlsanity.com/paper/2505.21748/) (2025)
- [Identifying Group Anchors in Real-World Group Interactions Under Label Scarcity](https://mlsanity.com/paper/2509.20762/) (2025)

## Abstract (arXiv)

We introduce a random hypergraph model for core-periphery structure. By leveraging our model's sufficient statistics, we develop a novel statistical inference algorithm that is able to scale to large hypergraphs with runtime that is practically linear wrt. the number of nodes in the graph after a preprocessing step that is almost linear in the number of hyperedges, as well as a scalable sampling algorithm. Our inference algorithm is capable of learning embeddings that correspond to the reputation (rank) of a node within the hypergraph. We also give theoretical bounds on the size of the core of hypergraphs generated by our model. We experiment with hypergraph data that range to ~ 105 hyperedges mined from the Microsoft Academic Graph, Stack Exchange, and GitHub and show that our model outperforms baselines wrt. producing good fits.