# Correlation Clustering with Asymmetric Classification Errors

@article{Jafarov2020CorrelationCW, title={Correlation Clustering with Asymmetric Classification Errors}, author={Jafar Jafarov and Sanchit Kalhan and Konstantin Makarychev and Yury Makarychev}, journal={ArXiv}, year={2020}, volume={abs/2108.05696} }

In the Correlation Clustering problem, we are given a weighted graph G with its edges labelled as “similar” or “dissimilar” by a binary classifier. The goal is to produce a clustering that minimizes the weight of “disagreements”: the sum of the weights of “similar” edges across clusters and “dissimilar” edges within clusters. We study the correlation clustering problem under the following assumption: Every “similar” edge e has weight we ∈ [αw,w] and every “dissimilar” edge e has weight we ≥ αw…

## 7 Citations

### Local Correlation Clustering with Asymmetric Classification Errors

- Computer ScienceICML
- 2021

An O ( (1/α)/2−/2p · log 1/α ) approximation algorithm is given for Correlation Clustering and an almost matching convex programming integrality gap is shown.

### Robust Correlation Clustering with Asymmetric Noise

- Computer ScienceArXiv
- 2021

It is demonstrated that l2-norm-diag recovers nodes with sufficiently strong cluster membership in graph instances generated by the NFM, thereby making progress towards establishing the provable robustness of the proposed algorithm.

### THE UNIVERSITY OF CHICAGO FOUR ALGORITHMS FOR CORRELATION CLUSTERING: A SURVEY A DISSERTATION SUBMITTED TO THE FACULTY OF THE DIVISION OF THE PHYSICAL SCIENCES IN CANDIDACY FOR THE DEGREE OF MASTERS

- Computer Science
- 2020

This exposition focuses on the case when G is complete and unweighted, and explores four approximation algorithms for the Correlation Clustering problem under this assumption.

### Correlation Clustering with Sherali-Adams

- Computer ScienceArXiv
- 2022

This paper shows that there exists a (1 . 994+ ε )-approximation algorithm based on O (1 /ε 2 ) rounds of the Sherali-Adams hierarchy and reaches an approximation ratio of 2 + ε for Correlation Clustering.

### Faster Deterministic Approximation Algorithms for Correlation Clustering and Cluster Deletion

- Computer ScienceArXiv
- 2021

This paper proves new relationships between correlation clustering problems and edge labeling problems related to the principle of strong triadic closure, and develops faster techniques that are purely combinatorial, based on computing maximal matchings in certain auxiliary graphs and hypergraphs.

### Correlation Clustering via Strong Triadic Closure Labeling: Fast Approximation Algorithms and Practical Lower Bounds

- Computer ScienceICML
- 2022

This work presents faster approximation algorithms that avoid linear programming relaxations, for two well-studied special cases: cluster editing and cluster deletion, by draw-ing new connections to edge labeling problems related to the principle of strong triadic closure.

### Differentially Private Correlation Clustering

- Computer ScienceICML
- 2021

An algorithm is proposed that achieves subquadratic additive error compared to the optimal cost and a lower bound is given showing that any pure differentially private algorithm for correlation clustering requires additive error of Ω(n).

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