# Robust Hypergraph Clustering via Convex Relaxation of Truncated MLE

arXiv: https://arxiv.org/abs/2003.10038 · HTML: https://mlsanity.com/paper/2003.10038/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2020.

## Built on by (most cited first, 7 of 7)

- [Exact Recovery in the General Hypergraph Stochastic Block Model](https://mlsanity.com/paper/2105.04770/) (2021)
- [Partial recovery and weak consistency in the non-uniform hypergraph Stochastic Block Model](https://mlsanity.com/paper/2112.11671/) (2021)
- [Sparse random hypergraphs: Non-backtracking spectra and community detection](https://mlsanity.com/paper/2203.07346/) (2022)
- [A Worker-Task Specialization Model for Crowdsourcing: Efficient Inference and Fundamental Limits](https://mlsanity.com/paper/2111.12550/) (2021)
- [Community Detection in the Hypergraph SBM: Exact Recovery Given the Similarity Matrix](https://mlsanity.com/paper/2208.12227/) (2022)
- [Multilayer hypergraph clustering using the aggregate similarity matrix](https://mlsanity.com/paper/2301.11657/) (2023)
- [Information-Theoretic Limits and Strong Consistency on Binary Non-uniform Hypergraph Stochastic Block Models](https://mlsanity.com/paper/2306.06845/) (2023)

## Abstract (arXiv)

We study hypergraph clustering in the weighted d-uniform hypergraph stochastic block model (dtextsf-WHSBM), where each edge consisting of d nodes from the same community has higher expected weight than the edges consisting of nodes from different communities. We propose a new hypergraph clustering algorithm, called textsfCRTMLE, and provide its performance guarantee under the dtextsf-WHSBM for general parameter regimes. We show that the proposed method achieves the order-wise optimal or the best existing results for approximately balanced community sizes. Moreover, our results settle the first recovery guarantees for growing number of clusters of unbalanced sizes. Involving theoretical analysis and empirical results, we demonstrate the robustness of our algorithm against the unbalancedness of community sizes or the presence of outlier nodes.