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Approximation algorithms for two variants of correlation clustering problem

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Abstract

Correlation clustering problem is a clustering problem which has many applications such as protein interaction networks, cross-lingual link detection, communication networks, and social computing. In this paper, we introduce two variants of correlation clustering problem: correlation clustering problem on uncertain graphs and correlation clustering problem with non-uniform hard constrained cluster sizes. Both problems overcome part of the limitations of the existing variants of correlation clustering problem and have practical applications in the real world. We provide a constant approximation algorithm and two approximation algorithms for the former and the latter problem, respectively.

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Acknowledgements

The first two authors are supported by National Natural Science Foundation of China (Nos. 11531014, 11871081). The third author is supported by Higher Educational Science and Technology Program of Shandong Province (No. J17KA171) and Natural Science Foundation of Shandong Province (No. ZR2019MA032) of China. The fourth author is supported by National Natural Science Foundation of China (No. 61433012).

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Authors and Affiliations

  1. Department of Operations Research and Information Engineering, Beijing University of Technology, Beijing, 100124, People’s Republic of China

    Sai Ji & Dachuan Xu

  2. School of Mathematics and Statistics, Shandong Normal University, 250358, Jinan, People’s Republic of China

    Min Li

  3. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, People’s Republic of China

    Yishui Wang

Authors
  1. Sai Ji
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  2. Dachuan Xu
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  3. Min Li
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  4. Yishui Wang
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Correspondence to Min Li.

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A preliminary version of this paper appeared in Proceedings of the 13th International Conference on Algorithmic Aspects in Information and Management, pp. 159-168, 2019.

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Ji, S., Xu, D., Li, M. et al. Approximation algorithms for two variants of correlation clustering problem. J Comb Optim 43, 933–952 (2022). https://doi.org/10.1007/s10878-020-00612-1

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