Cold-Start Active Correlation Clustering
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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.
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.
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References · 11
- 2020A Survey of Deep Active Learningcited by 554
- 2017Clustering with Noisy Queriescited by 34
- 2015A Survey of Signed Network Mining in Social Mediacited by 26
- 2019Correlation Clustering with Adaptive Similarity Queriescited by 7
- 2020Query-Efficient Correlation Clusteringcited by 7
- 2021Active Learning of Driving Scenario Trajectoriescited by 7
- 2024Query-Efficient Correlation Clustering with Noisy Oraclecited by 7
- 2023Correlation Clustering with Active Learning of Pairwise Similaritiescited by 5
- 2021Shift of Pairwise Similarities for Data Clusteringcited by 4
- 2018Learning Representations from Dendrogramscited by 3
- 2025An Efficient Local Search Approach for Polarized Community Discovery in Signed Networkscited by 1
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