IDEAS home Printed from https://ideas.repec.org/p/jgu/wpaper/1504.html
   My bibliography  Save this paper

Maximum Weight Relaxed Cliques and Russian Doll Search Revisited

Author

Listed:
  • Timo Gschwind

    (Johannes Gutenberg-Universität Mainz, Germany)

  • Stefan Irnich

    (Johannes Gutenberg-Universität Mainz, Germany)

  • Isabel Podlinski

    (Zalando SE, Germany)

Abstract

Trukhanov et al. [Trukhanov S, Balasubramaniam C, Balasundaram B, Butenko S (2013) Algorithms for detecting optimal hereditary structures in graphs, with application to clique relaxations. Comp. Opt. and Appl., 56(1), 113–130] used the Russian Doll Search (RDS) principle to effectively find maximum hereditary structures in graphs. Prominent examples of such hereditary structures are cliques and some clique relaxations intensely discussed and studied in network analysis. The effectiveness of the tailored RDS by Trukhanov et al. for s-plex and s-defective clique can be attributed to their cleverly designed incremental verification procedures used to distinguish feasible from infeasible structures. In this short note, we clarify the incompletely presented verification procedure for s-plex and present a new and simpler incremental verification procedure for s-defective cliques with a better worst-case runtime. Furthermore, we develop an incremental verification for s-bundle, giving rise to the first exact algorithm for solving the maximum cardinality and maximum weight s-bundle problems.

Suggested Citation

  • Timo Gschwind & Stefan Irnich & Isabel Podlinski, 2015. "Maximum Weight Relaxed Cliques and Russian Doll Search Revisited," Working Papers 1504, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz, revised 19 May 2015.
    • Handle: RePEc:jgu:wpaper:1504
    as

    Download full text from publisher

    File URL: https://download.uni-mainz.de/RePEc/pdf/Discussion_Paper_1504.pdf
    File Function: First version, 2015
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Svyatoslav Trukhanov & Chitra Balasubramaniam & Balabhaskar Balasundaram & Sergiy Butenko, 2013. "Algorithms for detecting optimal hereditary structures in graphs, with application to clique relaxations," Computational Optimization and Applications, Springer, vol. 56(1), pages 113-130, September.
    2. Pattillo, Jeffrey & Youssef, Nataly & Butenko, Sergiy, 2013. "On clique relaxation models in network analysis," European Journal of Operational Research, Elsevier, vol. 226(1), pages 9-18.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Timo Gschwind & Stefan Irnich & Fabio Furini & Roberto Wol?er Calvo, 2015. "Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques," Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhou, Yi & Lin, Weibo & Hao, Jin-Kao & Xiao, Mingyu & Jin, Yan, 2022. "An effective branch-and-bound algorithm for the maximum s-bundle problem," European Journal of Operational Research, Elsevier, vol. 297(1), pages 27-39.
    2. Timo Gschwind & Stefan Irnich & Fabio Furini & Roberto Wolfler Calvo, 2017. "A Branch-and-Price Framework for Decomposing Graphs into Relaxed Cliques," Working Papers 1723, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    3. Alexander Veremyev & Oleg A. Prokopyev & Sergiy Butenko & Eduardo L. Pasiliao, 2016. "Exact MIP-based approaches for finding maximum quasi-cliques and dense subgraphs," Computational Optimization and Applications, Springer, vol. 64(1), pages 177-214, May.
    4. Timo Gschwind & Stefan Irnich & Fabio Furini & Roberto Wol?er Calvo, 2015. "Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques," Working Papers 1520, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    5. Bruno Nogueira & Rian G. S. Pinheiro, 2020. "A GPU based local search algorithm for the unweighted and weighted maximum s-plex problems," Annals of Operations Research, Springer, vol. 284(1), pages 367-400, January.
    6. Timo Gschwind & Stefan Irnich & Fabio Furini & Roberto Wolfler Calvo, 2017. "Social Network Analysis and Community Detection by Decomposing a Graph into Relaxed Cliques," Working Papers 1722, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
    7. Svyatoslav Trukhanov & Chitra Balasubramaniam & Balabhaskar Balasundaram & Sergiy Butenko, 2013. "Algorithms for detecting optimal hereditary structures in graphs, with application to clique relaxations," Computational Optimization and Applications, Springer, vol. 56(1), pages 113-130, September.
    8. Juan Ma & Foad Mahdavi Pajouh & Balabhaskar Balasundaram & Vladimir Boginski, 2016. "The Minimum Spanning k -Core Problem with Bounded CVaR Under Probabilistic Edge Failures," INFORMS Journal on Computing, INFORMS, vol. 28(2), pages 295-307, May.
    9. Veremyev, Alexander & Boginski, Vladimir & Pasiliao, Eduardo L. & Prokopyev, Oleg A., 2022. "On integer programming models for the maximum 2-club problem and its robust generalizations in sparse graphs," European Journal of Operational Research, Elsevier, vol. 297(1), pages 86-101.
    10. Komusiewicz, Christian & Nichterlein, André & Niedermeier, Rolf & Picker, Marten, 2019. "Exact algorithms for finding well-connected 2-clubs in sparse real-world graphs: Theory and experiments," European Journal of Operational Research, Elsevier, vol. 275(3), pages 846-864.
    11. Niels Grüttemeier & Philipp Heinrich Keßler & Christian Komusiewicz & Frank Sommer, 2024. "Efficient branch-and-bound algorithms for finding triangle-constrained 2-clubs," Journal of Combinatorial Optimization, Springer, vol. 48(3), pages 1-27, October.
    12. Maciej Rysz & Mohammad Mirghorbani & Pavlo Krokhmal & Eduardo L. Pasiliao, 2014. "On risk-averse maximum weighted subgraph problems," Journal of Combinatorial Optimization, Springer, vol. 28(1), pages 167-185, July.
    13. Farasat, Alireza & Nikolaev, Alexander G., 2016. "Signed social structure optimization for shift assignment in the nurse scheduling problem," Socio-Economic Planning Sciences, Elsevier, vol. 56(C), pages 3-13.
    14. Şuvak, Zeynep & Altınel, İ. Kuban & Aras, Necati, 2020. "Exact solution algorithms for the maximum flow problem with additional conflict constraints," European Journal of Operational Research, Elsevier, vol. 287(2), pages 410-437.
    15. Chitra Balasubramaniam & Sergiy Butenko, 2017. "On robust clusters of minimum cardinality in networks," Annals of Operations Research, Springer, vol. 249(1), pages 17-37, February.
    16. Zeynep Ertem & Eugene Lykhovyd & Yiming Wang & Sergiy Butenko, 2020. "The maximum independent union of cliques problem: complexity and exact approaches," Journal of Global Optimization, Springer, vol. 76(3), pages 545-562, March.
    17. Maciej Rysz & Foad Mahdavi Pajouh & Pavlo Krokhmal & Eduardo L. Pasiliao, 2018. "Identifying risk-averse low-diameter clusters in graphs with stochastic vertex weights," Annals of Operations Research, Springer, vol. 262(1), pages 89-108, March.
    18. Furini, Fabio & Ljubić, Ivana & Martin, Sébastien & San Segundo, Pablo, 2019. "The maximum clique interdiction problem," European Journal of Operational Research, Elsevier, vol. 277(1), pages 112-127.
    19. Buchanan, Austin & Sung, Je Sang & Boginski, Vladimir & Butenko, Sergiy, 2014. "On connected dominating sets of restricted diameter," European Journal of Operational Research, Elsevier, vol. 236(2), pages 410-418.
    20. Zhuqi Miao & Balabhaskar Balasundaram, 2020. "An Ellipsoidal Bounding Scheme for the Quasi-Clique Number of a Graph," INFORMS Journal on Computing, INFORMS, vol. 32(3), pages 763-778, July.

    More about this item

    Keywords

    Relaxed clique; Russian Doll Search; Optimal hereditary structures; Maximum weight problem;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jgu:wpaper:1504. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Research Unit IPP (email available below). General contact details of provider: https://edirc.repec.org/data/vlmaide.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.