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Regular Paper

2k-Vertex Kernels for Cluster Deletion and Strong Triadic Closure

School of Information, Guangdong University of Finance and Economics, Guangzhou 510320, China
Department of Computer Science, Rutgers University, Piscataway 08854, U.S.A.
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Abstract

Cluster deletion and strong triadic closure are two important NP-complete problems that have received significant attention due to their applications in various areas, including social networks and data analysis. Although cluster deletion and strong triadic closure are closely linked by induced paths on three vertices, there are subtle differences between them. In some cases, the solutions of strong triadic closure and cluster deletion are quite different. In this paper, we study the parameterized algorithms for these two problems. More specifically, we focus on the kernels of these two problems. Instead of separating the critical clique and its neighbors for analysis, we consider them as a whole, which allows us to more effectively bound the number of related vertices. In addition, in analyzing the kernel of strong triadic closure, we introduce the concept of edge-disjoint induced path on three vertices, which enables us to obtain the lower bound of weak edge number in a more concise way. Our analysis demonstrates that cluster deletion and strong triadic closure both admit 2k-vertex kernels. These results represent improvements over previously best-known kernels for both problems. Furthermore, our analysis provides additional insights into the relationship between cluster deletion and strong triadic closure.

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References

[1]
Sintos S, Tsaparas P. Using strong triadic closure to characterize ties in social networks. In Proc. the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug. 2014, pp.1466–1475. DOI: 10.1145/2623330.2623664.
[2]

Granovetter M S. The strength of weak ties. American Journal of Sociology, 1973, 78(6): 1360–1380. DOI: 10.1086/ 225469.

[3]

Golovach P A, Heggernes P, Konstantinidis A L, Lima P T, Papadopoulos C. Parameterized aspects of strong subgraph closure. Algorithmica, 2020, 82(7): 2006–2038. DOI: 10.1007/s00453-020-00684-9.

[4]

Grüttemeier N, Komusiewicz C. On the relation of strong triadic closure and cluster deletion. Algorithmica, 2020, 82(4): 853–880. DOI: 10.1007/s00453-019-00617-1.

[5]

Konstantinidis A L, Nikolopoulos S D, Papadopoulos C. Strong triadic closure in cographs and graphs of low maximum degree. Theoretical Computer Science, 2018, 740: 76–84. DOI: 10.1016/j.tcs.2018.05.012.

[6]

Guo J. A more effective linear kernelization for cluster editing. Theoretical Computer Science, 2009, 410(8/9/10): 718–726. DOI: 10.1016/j.tcs.2008.10.021.

[7]

Chen J E, Meng J. A 2 k kernel for the cluster editing problem. Journal of Computer and System Sciences, 2012, 78(1): 211–220. DOI: 10.1016/j.jcss.2011.04.001.

[8]
Berkhin P. A survey of clustering data mining techniques. In Grouping Multidimensional Data: Recent Advances in Clustering, Kogan J, Nicholas C, Teboulle M (eds.), Springer, 2006, pp.25–71. DOI: 10.1007/3-540-28349-8_2.
[9]

Bansal N, Blum A, Chawla S. Correlation clustering. Machine Learning, 2004, 56(1/2/3): 89–113. DOI: 10.1023/B:MACH.0000033116.57574.95.

[10]

Chen Z Z, Jiang T, Lin G H. Computing phylogenetic roots with bounded degrees and errors. SIAM Journal on Computing, 2003, 32(4): 864–879. DOI: 10.1137/S0097 539701389154.

[11]

Shamir R, Sharan R, Tsur D. Cluster graph modification problems. Discrete Applied Mathematics, 2004, 144(1/2): 173–182. DOI: 10.1016/j.dam.2004.01.007.

[12]

Gao Y, Hare D R, Nastos J. The cluster deletion problem for cographs. Discrete Mathematics, 2013, 313(23): 2763–2771. DOI: 10.1016/j.disc.2013.08.017.

[13]

Ben-Dor A, Shamir R, Yakhini Z. Clustering gene expression patterns. Journal of Computational Biology, 1999, 6(3/4): 281–297. DOI: 10.1089/106652799318274.

[14]
Fellows M R. The lost continent of polynomial time: Preprocessing and kernelization. In Proc. the 2nd International Workshop on Parameterized and Exact Computation, Sept. 2006, pp.276–277. DOI: 10.1007/11847250_25.
[15]

Konstantinidis A L, Papadopoulos C. Maximizing the strong triadic closure in split graphs and proper interval graphs. Discrete Applied Mathematics, 2020, 285: 79–95. DOI: 10.1016/j.dam.2020.05.035.

[16]
Hsu W L, Ma T H. Substitution decomposition on chordal graphs and applications. In Proc. the 2nd International Symposium on Algorithms, Dec. 1991, pp.52–60. DOI: 10.1007/3-540-54945-5_49.
[17]
Niedermeier R. Invitation to Fixed-Parameter Algorithms. Oxford University Press, 2006. DOI: 10.1093/acprof:oso/9780198566076.001.0001.
[18]
Cygan M, Fomin F V, Kowalik Ł, Lokshtanov D, Marx D, Pilipczuk M, Pilipczuk M, Saurabh S. Parameterized Algorithms. Springer, 2015. DOI: 10.1007/978-3-319-21275-3.
[19]

Protti F, Da Silva M D, Szwarcfiter J L. Applying modular decomposition to parameterized cluster editing problems. Theory of Computing Systems, 2009, 44(1): 91–104. DOI: 10.1007/s00224-007-9032-7.

Journal of Computer Science and Technology
Pages 1431-1439
Cite this article:
Gao W-Y, Gao H. 2k-Vertex Kernels for Cluster Deletion and Strong Triadic Closure. Journal of Computer Science and Technology, 2023, 38(6): 1431-1439. https://doi.org/10.1007/s11390-023-1420-1

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Received: 04 March 2021
Accepted: 07 June 2023
Published: 15 November 2023
© Institute of Computing Technology, Chinese Academy of Sciences 2023
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