# Local Correlation Clustering with Asymmetric Classification Errors

@article{Jafarov2021LocalCC, title={Local Correlation Clustering with Asymmetric Classification Errors}, author={Jafar Jafarov and Sanchit Kalhan and Konstantin Makarychev and Yury Makarychev}, journal={ArXiv}, year={2021}, volume={abs/2108.05697} }

In the Correlation Clustering problem, we are given a complete weighted graph G with its edges labeled as “similar” and “dissimilar” by a noisy binary classifier. For a clustering C of graph G, a similar edge is in disagreement with C, if its endpoints belong to distinct clusters; and a dissimilar edge is in disagreement with C if its endpoints belong to the same cluster. The disagreements vector, dis, is a vector indexed by the vertices of G such that the v-th coordinate disv equals the weight…

## 5 Citations

### 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.

### Sublinear Time and Space Algorithms for Correlation Clustering via Sparse-Dense Decompositions

- Computer ScienceITCS
- 2022

A new approach for solving (minimum disagreement) correlation clustering that results in sublinear algorithms with highly efficient time and space complexity for this problem is presented, with a novel connection to sparse-dense graph decompositions that are used extensively in the graph coloring literature.

### 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.

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This paper presents the first known algorithm for minimizing the \ell_q norm of the disagreements vector on arbitrary graphs and provides an improved algorithm for minimize the\ell-q norm (q >= 1) of the disagreement vector on complete graphs.

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