# Cold-Start Active Correlation Clustering

arXiv: https://arxiv.org/abs/2509.25376 · HTML: https://mlsanity.com/paper/2509.25376/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 32 references that are in the map.
- First posted on arXiv 29 Sep 2025.

## Builds on (most cited first, 11 of 11)

- [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)
- [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)
- [Query-Efficient Correlation Clustering with Noisy Oracle](https://mlsanity.com/paper/2402.01400/) (2024)
- [Correlation Clustering with Active Learning of Pairwise Similarities](https://mlsanity.com/paper/2302.10295/) (2023)
- [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)
- [An Efficient Local Search Approach for Polarized Community Discovery in Signed Networks](https://mlsanity.com/paper/2502.02197/) (2025)

## Abstract (arXiv)

We study active correlation clustering where pairwise similarities are not provided upfront and must be queried in a cost-efficient manner through active learning. Specifically, we focus on the cold-start scenario, where no true initial pairwise similarities are available for active learning. To address this challenge, we propose a coverage-aware method that encourages diversity early in the process. We demonstrate the effectiveness of our approach through several synthetic and real-world experiments.