# Correlation Clustering with Constrained Cluster Sizes and Extended Weights Bounds

arXiv: https://arxiv.org/abs/1411.0547 · HTML: https://mlsanity.com/paper/1411.0547/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2014.

## Built on by (most cited first, 6 of 6)

- [Correlation Clustering via Strong Triadic Closure Labeling: Fast Approximation Algorithms and Practical Lower Bounds](https://mlsanity.com/paper/2111.10699/) (2021)
- [Exact Community Recovery over Signed Graphs](https://mlsanity.com/paper/2202.12255/) (2022)
- [Combinatorial Approximations for Cluster Deletion: Simpler, Faster, and Better](https://mlsanity.com/paper/2404.16131/) (2024)
- [Multilayer Correlation Clustering](https://mlsanity.com/paper/2404.16676/) (2024)
- [Online Correlation Clustering: Simultaneously Optimizing All ℓp-norms](https://mlsanity.com/paper/2510.15076/) (2025)
- [Correlation Clustering with Random Partial Information](https://mlsanity.com/paper/2608.16315/) (2026)

## Abstract (arXiv)

We consider the problem of correlation clustering on graphs with constraints on both the cluster sizes and the positive and negative weights of edges. Our contributions are twofold: First, we introduce the problem of correlation clustering with bounded cluster sizes. Second, we extend the regime of weight values for which the clustering may be performed with constant approximation guarantees in polynomial time and apply the results to the bounded cluster size problem.