# 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} }

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

### Differentially-Private Hierarchical Clustering with Provable Approximation Guarantees

- 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.

### Correlation Clustering with Sherali-Adams

- Computer Science2022 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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