Cluster Editing Parameterized Above Modification-Disjoint P₃-Packings

Authors Shaohua Li, Marcin Pilipczuk, Manuel Sorge



PDF
Thumbnail PDF

File

LIPIcs.STACS.2021.49.pdf
  • Filesize: 1.04 MB
  • 16 pages

Document Identifiers

Author Details

Shaohua Li
  • Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Poland
Marcin Pilipczuk
  • Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Poland
Manuel Sorge
  • Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Poland
  • Institute of Logic and Computation, TU Wien, Austria

Cite AsGet BibTex

Shaohua Li, Marcin Pilipczuk, and Manuel Sorge. Cluster Editing Parameterized Above Modification-Disjoint P₃-Packings. In 38th International Symposium on Theoretical Aspects of Computer Science (STACS 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 187, pp. 49:1-49:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)
https://doi.org/10.4230/LIPIcs.STACS.2021.49

Abstract

Given a graph G = (V,E) and an integer k, the Cluster Editing problem asks whether we can transform G into a union of vertex-disjoint cliques by at most k modifications (edge deletions or insertions). In this paper, we study the following variant of Cluster Editing. We are given a graph G = (V,E), a packing ℋ of modification-disjoint induced P₃s (no pair of P₃s in H share an edge or non-edge) and an integer 𝓁. The task is to decide whether G can be transformed into a union of vertex-disjoint cliques by at most 𝓁+|H| modifications (edge deletions or insertions). We show that this problem is NP-hard even when 𝓁 = 0 (in which case the problem asks to turn G into a disjoint union of cliques by performing exactly one edge deletion or insertion per element of H) and when each vertex is in at most 23 P₃s of the packing. This answers negatively a question of van Bevern, Froese, and Komusiewicz (CSR 2016, ToCS 2018), repeated by C. Komusiewicz at Shonan meeting no. 144 in March 2019. We then initiate the study to find the largest integer c such that the problem remains tractable when restricting to packings such that each vertex is in at most c packed P₃s. Van Bevern et al. showed that the case c = 1 is fixed-parameter tractable with respect to 𝓁 and we show that the case c = 2 is solvable in |V|^{2𝓁 + O(1)} time.

Subject Classification

ACM Subject Classification
  • Mathematics of computing → Graph algorithms
Keywords
  • Graph algorithms
  • fixed-parameter tractability
  • parameterized complexity

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads

References

  1. Nir Ailon, Moses Charikar, and Alantha Newman. Aggregating inconsistent information: Ranking and clustering. J. ACM, 55(5):23:1-23:27, 2008. URL: https://doi.org/10.1145/1411509.1411513.
  2. Takuya Akiba and Yoichi Iwata. Branch-and-reduce exponential/FPT algorithms in practice: A case study of vertex cover. Theoretical Computer Science, 609:211-225, 2016. URL: https://doi.org/10.1016/j.tcs.2015.09.023.
  3. Noga Alon, Konstantin Makarychev, Yury Makarychev, and Assaf Naor. Quadratic forms on graphs. In Harold N. Gabow and Ronald Fagin, editors, Proceedings of the 37th Annual ACM Symposium on Theory of Computing (STOC 2005), pages 486-493. ACM, 2005. URL: https://doi.org/10.1145/1060590.1060664.
  4. Sanjeev Arora, Eli Berger, Elad Hazan, Guy Kindler, and Muli Safra. On non-approximability for quadratic programs. Electronic Colloquium on Computational Complexity (ECCC), 058, 2005. URL: http://eccc.hpi-web.de/eccc-reports/2005/TR05-058/index.html.
  5. Nikhil Bansal, Avrim Blum, and Shuchi Chawla. Correlation clustering. Machine Learning, 56(1-3):89-113, 2004. URL: https://doi.org/10.1023/B:MACH.0000033116.57574.95.
  6. Amir Ben-Dor, Ron Shamir, and Zohar Yakhini. Clustering gene expression patterns. Journal of Computational Biology, 6(3/4):281-297, 1999. URL: https://doi.org/10.1089/106652799318274.
  7. S. Böcker, S. Briesemeister, Q.B.A. Bui, and A. Truss. Going weighted: Parameterized algorithms for cluster editing. Theoretical Computer Science, 410(52):5467-5480, 2009. URL: https://doi.org/10.1016/j.tcs.2009.05.006.
  8. Sebastian Böcker. A golden ratio parameterized algorithm for cluster editing. Journal of Discrete Algorithms, 16:79-89, 2012. URL: https://doi.org/10.1016/j.jda.2012.04.005.
  9. Sebastian Böcker and Jan Baumbach. Cluster editing. In Paola Bonizzoni, Vasco Brattka, and Benedikt Löwe, editors, Proceedings of the 9th Conference on Computability in Europe (CiE 2013), volume 7921 of Lecture Notes in Computer Science, pages 33-44. Springer, 2013. URL: https://doi.org/10.1007/978-3-642-39053-1_5.
  10. Sebastian Böcker, Sebastian Briesemeister, and Gunnar W. Klau. Exact algorithms for cluster editing: Evaluation and experiments. Algorithmica, 60(2):316-334, 2011. URL: https://doi.org/10.1007/s00453-009-9339-7.
  11. Sebastian Böcker and Peter Damaschke. Even faster parameterized cluster deletion and cluster editing. Information Processing Letters, 111(14):717-721, 2011. URL: https://doi.org/10.1016/j.ipl.2011.05.003.
  12. Hans L. Bodlaender, Michael R. Fellows, Pinar Heggernes, Federico Mancini, Charis Papadopoulos, and Frances A. Rosamond. Clustering with partial information. Theoretical Computer Science, 411(7-9):1202-1211, 2010. URL: https://doi.org/10.1016/j.tcs.2009.12.016.
  13. Nicolas Bousquet, Jean Daligault, and Stéphan Thomassé. Multicut is FPT. SIAM Journal on Computing, 47(1):166-207, 2018. URL: https://doi.org/10.1137/140961808.
  14. Yixin Cao and Jianer Chen. Cluster editing: Kernelization based on edge cuts. Algorithmica, 64(1):152-169, 2012. URL: https://doi.org/10.1007/s00453-011-9595-1.
  15. Moses Charikar, Venkatesan Guruswami, and Anthony Wirth. Clustering with qualitative information. Journal of Computer and System Sciences, 71(3):360-383, 2005. URL: https://doi.org/10.1016/j.jcss.2004.10.012.
  16. Jianer Chen and Jie Meng. A 2k kernel for the cluster editing problem. Journal of Computer and System Sciences, 78(1):211-220, 2012. URL: https://doi.org/10.1016/j.jcss.2011.04.001.
  17. Christophe Crespelle, Pål Grønås Drange, Fedor V. Fomin, and Petr A. Golovach. A survey of parameterized algorithms and the complexity of edge modification. arXiv:2001.06867 [cs], 2020. URL: http://arxiv.org/abs/2001.06867.
  18. Marek Cygan, Marcin Pilipczuk, Michal Pilipczuk, and Jakub Onufry Wojtaszczyk. On multiway cut parameterized above lower bounds. ACM Transactions on Computation Theory, 5(1):3:1-3:11, 2013. URL: https://doi.org/10.1145/2462896.2462899.
  19. Peter Damaschke. Fixed-parameter enumerability of cluster editing and related problems. Theory of Computing Systems, 46(2):261-283, 2010. URL: https://doi.org/10.1007/s00224-008-9130-1.
  20. Michael R. Fellows. The lost continent of polynomial time: Preprocessing and kernelization. In Hans L. Bodlaender and Michael A. Langston, editors, Proceedings of the Second International Workshop on Parameterized and Exact Computation (IWPEC 2006), volume 4169 of Lecture Notes in Computer Science, pages 276-277. Springer, 2006. URL: https://doi.org/10.1007/11847250_25.
  21. Michael R. Fellows, Jiong Guo, Christian Komusiewicz, Rolf Niedermeier, and Johannes Uhlmann. Graph-based data clustering with overlaps. Discrete Optimization, 8(1):2-17, 2011. URL: https://doi.org/10.1016/j.disopt.2010.09.006.
  22. Michael R. Fellows, Michael A. Langston, Frances A. Rosamond, and Peter Shaw. Efficient parameterized preprocessing for cluster editing. In Erzsébet Csuhaj-Varjú and Zoltán Ésik, editors, Proceedings of the 16th International Symposium on Fundamentals of Computation Theory (FCT 2007), volume 4639 of Lecture Notes in Computer Science, pages 312-321. Springer, 2007. URL: https://doi.org/10.1007/978-3-540-74240-1_27.
  23. Fedor V. Fomin, Stefan Kratsch, Marcin Pilipczuk, Michał Pilipczuk, and Yngve Villanger. Subexponential fixed-parameter tractability of cluster editing. arXiv:1112.4419 [cs], 2013. URL: http://arxiv.org/abs/1112.4419.
  24. Fedor V. Fomin, Stefan Kratsch, Marcin Pilipczuk, Michał Pilipczuk, and Yngve Villanger. Tight bounds for parameterized complexity of cluster editing with a small number of clusters. Journal of Computer and System Sciences, 80(7):1430-1447, 2014. URL: https://doi.org/10.1016/j.jcss.2014.04.015.
  25. Vincent Froese. Fine-Grained Complexity Analysis of Some Combinatorial Data Science Problems. PhD thesis, Technische Universität Berlin, 2018. URL: https://doi.org/10.14279/depositonce-7123.
  26. Shivam Garg and Geevarghese Philip. Raising the bar for vertex cover: Fixed-parameter tractability above A higher guarantee. In Robert Krauthgamer, editor, Proceedings of the Twenty-Seventh Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2016), pages 1152-1166. SIAM, 2016. URL: https://doi.org/10.1137/1.9781611974331.ch80.
  27. Jens Gramm, Jiong Guo, Falk Hüffner, and Rolf Niedermeier. Automated generation of search tree algorithms for hard graphmodification problems. Algorithmica, 39(4):321-347, 2004. URL: https://doi.org/10.1007/s00453-004-1090-5.
  28. Jens Gramm, Jiong Guo, Falk Hüffner, and Rolf Niedermeier. Graph-modeled data clustering: Exact algorithms for clique generation. Theory of Computing Systems, 38(4):373-392, 2005. URL: https://doi.org/10.1007/s00224-004-1178-y.
  29. Jiong Guo. A more effective linear kernelization for cluster editing. Theoretical Computer Science, 410(8):718-726, 2009. URL: https://doi.org/10.1016/j.tcs.2008.10.021.
  30. Jiong Guo, Iyad A. Kanj, Christian Komusiewicz, and Johannes Uhlmann. Editing graphs into disjoint unions of dense clusters. Algorithmica, 61(4):949-970, 2011. URL: https://doi.org/10.1007/s00453-011-9487-4.
  31. Jiong Guo, Christian Komusiewicz, Rolf Niedermeier, and Johannes Uhlmann. A more relaxed model for graph-based data clustering: s-plex cluster editing. SIAM Journal on Discrete Mathematics, 24(4):1662-1683, 2010. URL: https://doi.org/10.1137/090767285.
  32. Yoichi Iwata. Linear-time kernelization for feedback vertex set. In Ioannis Chatzigiannakis, Piotr Indyk, Fabian Kuhn, and Anca Muscholl, editors, Proceedings of the 44th International Colloquium on Automata, Languages, and Programming (ICALP 2017), volume 80 of Leibniz International Proceedings in Informatics (LIPIcs), pages 68:1-68:14. Schloss Dagstuhlendash Leibniz-Zentrum fuer Informatik, 2017. URL: https://doi.org/10.4230/LIPIcs.ICALP.2017.68.
  33. Bart M.P. Jansen, Christian Schulz, and Hisao Tamaki. NII shonan meeting report no. 144 parameterized graph algorithms and data reduction, 2019. URL: https://shonan.nii.ac.jp/docs/No.144.pdf.
  34. Krzysztof Kiljan and Marcin Pilipczuk. Experimental evaluation of parameterized algorithms for feedback vertex set. In Gianlorenzo D'Angelo, editor, Proceedings of the 17th International Symposium on Experimental Algorithms (SEA 2018), volume 103 of Leibniz International Proceedings in Informatics (LIPIcs), pages 12:1-12:12, Dagstuhl, Germany, 2018. Schloss Dagstuhlendash Leibniz-Zentrum fuer Informatik. URL: https://doi.org/10.4230/LIPIcs.SEA.2018.12.
  35. Christian Komusiewicz and Johannes Uhlmann. Cluster editing with locally bounded modifications. Discrete Applied Mathematics, 160(15):2259-2270, 2012. URL: https://doi.org/10.1016/j.dam.2012.05.019.
  36. Stefan Kratsch. A randomized polynomial kernelization for vertex cover with a smaller parameter. SIAM Journal on Discrete Mathematics, 32(3):1806-1839, 2018. URL: https://doi.org/10.1137/16M1104585.
  37. Shaohua Li, Marcin Pilipczuk, and Manuel Sorge. Cluster editing parameterized above modification-disjoint P₃-packings. arXiv:1910.08517 [cs], 2019. URL: http://arxiv.org/abs/1910.08517.
  38. Daniel Lokshtanov, N. S. Narayanaswamy, Venkatesh Raman, M. S. Ramanujan, and Saket Saurabh. Faster parameterized algorithms using linear programming. ACM Transactions on Algorithms, 11(2):15:1-15:31, 2014. URL: https://doi.org/10.1145/2566616.
  39. Meena Mahajan and Venkatesh Raman. Parameterizing above guaranteed values: Maxsat and maxcut. Journal of Algorithms, 31(2):335-354, 1999. URL: https://doi.org/10.1006/jagm.1998.0996.
  40. Dániel Marx and Igor Razgon. Fixed-parameter tractability of multicut parameterized by the size of the cutset. SIAM Journal on Computing, 43(2):355-388, 2014. URL: https://doi.org/10.1137/110855247.
  41. John H. Morris, Leonard Apeltsin, Aaron M. Newman, Jan Baumbach, Tobias Wittkop, Gang Su, Gary D. Bader, and Thomas E. Ferrin. clusterMaker: a multi-algorithm clustering plugin for Cytoscape. BMC Bioinformatics, 12(1):436, 2011. URL: https://doi.org/10.1186/1471-2105-12-436.
  42. Fábio Protti, Maise Dantas da Silva, and Jayme Luiz Szwarcfiter. Applying modular decomposition to parameterized cluster editing problems. Theory of Computing Systems, 44(1):91-104, 2009. URL: https://doi.org/10.1007/s00224-007-9032-7.
  43. Ron Shamir, Roded Sharan, and Dekel Tsur. Cluster graph modification problems. Discrete Applied Mathematics, 144(1-2):173-182, 2004. URL: https://doi.org/10.1016/j.dam.2004.01.007.
  44. René van Bevern, Vincent Froese, and Christian Komusiewicz. Parameterizing edge modification problems above lower bounds. Theory of Computing Systems, 62(3):739-770, 2018. URL: https://doi.org/10.1007/s00224-016-9746-5.
  45. Tobias Wittkop, Dorothea Emig, Sita Lange, Sven Rahmann, Mario Albrecht, John H. Morris, Sebastian Böcker, Jens Stoye, and Jan Baumbach. Partitioning biological data with transitivity clustering. Nature Methods, 7(6):419-420, 2010. URL: https://doi.org/10.1038/nmeth0610-419.
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail