# Correlation Clustering with Active Learning of Pairwise Similarities

arXiv: https://arxiv.org/abs/2302.10295 · HTML: https://mlsanity.com/paper/2302.10295/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 53 references that are in the map.
- First posted on arXiv 20 Feb 2023.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Survey of Deep Active Learning](https://mlsanity.com/paper/2009.00236/) (2020)
- [Clustering with Noisy Queries](https://mlsanity.com/paper/1706.07510/) (2017)
- [A Survey of Signed Network Mining in Social Media](https://mlsanity.com/paper/1511.07569/) (2015)
- [Query Complexity of Clustering with Side Information](https://mlsanity.com/paper/1706.07719/) (2017)
- [Correlation Clustering with Same-Cluster Queries Bounded by Optimal Cost](https://mlsanity.com/paper/1908.04976/) (2019)
- [Local algorithms for interactive clustering](https://mlsanity.com/paper/1312.6724/) (2013)
- [Active Learning for Sound Event Detection](https://mlsanity.com/paper/2002.05033/) (2020)
- [Differentially Private Correlation Clustering](https://mlsanity.com/paper/2102.08885/) (2021)
- [Approximate Correlation Clustering Using Same-Cluster Queries](https://mlsanity.com/paper/1712.06865/) (2017)
- [COBRA: A Fast and Simple Method for Active Clustering with Pairwise Constraints](https://mlsanity.com/paper/1801.09955/) (2018)
- [Active Learning with Logged Data](https://mlsanity.com/paper/1802.09069/) (2018)
- [Correlation Clustering with Adaptive Similarity Queries](https://mlsanity.com/paper/1905.11902/) (2019)
- [Query-Efficient Correlation Clustering](https://mlsanity.com/paper/2002.11557/) (2020)
- [Active Learning of Driving Scenario Trajectories](https://mlsanity.com/paper/2108.03217/) (2021)
- [Local correlation clustering](https://mlsanity.com/paper/1312.5105/) (2013)
- [Shift of Pairwise Similarities for Data Clustering](https://mlsanity.com/paper/2110.13103/) (2021)
- [Learning Representations from Dendrograms](https://mlsanity.com/paper/1812.09225/) (2018)

## Built on by (most cited first, 5 of 5)

- [Query-Efficient Correlation Clustering with Noisy Oracle](https://mlsanity.com/paper/2402.01400/) (2024)
- [Information-Theoretic Active Correlation Clustering](https://mlsanity.com/paper/2402.03587/) (2024)
- [An Efficient Local Search Approach for Polarized Community Discovery in Signed Networks](https://mlsanity.com/paper/2502.02197/) (2025)
- [Multilayer Correlation Clustering](https://mlsanity.com/paper/2404.16676/) (2024)
- [Cold-Start Active Correlation Clustering](https://mlsanity.com/paper/2509.25376/) (2025)

## Abstract (arXiv)

Correlation clustering is a well-known unsupervised learning setting that deals with positive and negative pairwise similarities. In this paper, we study the case where the pairwise similarities are not given in advance and must be queried in a cost-efficient way. Thereby, we develop a generic active learning framework for this task that benefits from several advantages, e.g., flexibility in the type of feedback that a user/annotator can provide, adaptation to any correlation clustering algorithm and query strategy, and robustness to noise. In addition, we propose and analyze a number of novel query strategies suited to this setting. We demonstrate the effectiveness of our framework and the proposed query strategies via several experimental studies.