Approximation Algorithms for Graph Clustering Problems withClusters of Bounded Size

In the cluster editing problem, one has to partition the set of vertices of a graph into pairwise disjoint subsets (called clusters) minimizing the number of edges between clusters and the number of missing edges within clusters. We consider a version of the problem in which cluster sizes are bounded from above by a positive integer $$ s $$ . This problem is NP-hard for any fixed $$ s \geqslant 3 $$ . We propose polynomial-time approximation algorithms for this version of the problem. Their performance guarantees equal $$ 5/3 $$ for the case $$ s = 3 $$ and $$ 2 $$ for $$ s = 4 $$ . We also show that the cluster editing problem is APX-hard for the case of $$ s = 3 $$ .

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Publication Details

Journal
Journal of Applied and Industrial Mathematics
Published
2024-12-01
DOI
https://doi.org/10.1134/s1990478924040069
Primary Topic
Advanced Clustering Algorithms Research
Type
article
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article

Approximation Algorithms for Graph Clustering Problems withClusters of Bounded Size

Svetlana Il’eva, Alexander Kononov, Victor Il’ev
Journal of Applied and Industrial Mathematics
Advanced Clustering Algorithms Research
article

Approximation Algorithms for Graph Clustering Problems withClusters of Bounded Size

Svetlana Il’eva, Alexander Kononov, Victor Il’ev
article en

Abstract

In the cluster editing problem, one has to partition the set of vertices of a graph into pairwise disjoint subsets (called clusters) minimizing the number of edges between clusters and the number of missing edges within clusters. We consider a version of the problem in which cluster sizes are bounded from above by a positive integer $$ s $$ . This problem is NP-hard for any fixed $$ s \geqslant 3 $$ . We propose polynomial-time approximation algorithms for this version of the problem. Their performance guarantees equal $$ 5/3 $$ for the case $$ s = 3 $$ and $$ 2 $$ for $$ s = 4 $$ . We also show that the cluster editing problem is APX-hard for the case of $$ s = 3 $$ .

Journal of Applied and Industrial MathematicsVol. 18(4)
Russian Academy of Sciences (RU), Sobolev Institute of Mathematics (RU)
Openalex Percentile: Top 72%
Advanced Clustering Algorithms Research
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