# Near-Optimal Correlation Clustering with Privacy

@article{CohenAddad2022NearOptimalCC,
title={Near-Optimal Correlation Clustering with Privacy},
author={Vincent Cohen-Addad and Chenglin Fan and Silvio Lattanzi and Slobodan Mitrovi'c and Ashkan Norouzi-Fard and Nikos Parotsidis and Jakub Tarnawski},
journal={ArXiv},
year={2022},
volume={abs/2203.01440}
}
• Published 2 March 2022
• Computer Science
• ArXiv
Correlation clustering is a central problem in unsupervised learning, with applications spanning community detection, duplicate detection, automated labelling and many more. In the correlation clustering problem one receives as input a set of nodes and for each node a list of co-clustering preferences, and the goal is to output a clustering that minimizes the disagreement with the specified nodes’ preferences. In this paper, we introduce a simple and computationally efficient algorithm for the…
2 Citations
• Computer Science
• 2023
This work focuses on the stochastic block model, a popular model of graphs, and proposes a private 1 + o (1) approximation algorithm which also recovers the blocks exactly and meets the lower bound.
• Computer Science
2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS)
• 2022
This paper affirmatively shows that there exists a $1.994-approximation algorithm based on$O(1/\varepsilon^{2})$rounds of the Sherali-Adams hierarchy, and reaches an approximation ratio of$2+\varpsilon\$ for CORRELATION CLUSTERING.

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