IDEAS home Printed from https://ideas.repec.org/a/inm/orijoc/v34y2022i3p1309-1326.html
   My bibliography  Save this article

Solving the Distance-Based Critical Node Problem

Author

Listed:
  • Hosseinali Salemi

    (Department of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, Iowa 50011)

  • Austin Buchanan

    (School of Industrial Engineering and Management, Oklahoma State University, Stillwater, Oklahoma 74078)

Abstract

In critical node problems, the task is to identify a small subset of so-called critical nodes whose deletion maximally degrades a network’s “connectivity” (however that is measured). Problems of this type have been widely studied, for example, for limiting the spread of infectious diseases. However, existing approaches for solving them have typically been limited to networks having fewer than 1,000 nodes. In this paper, we consider a variant of this problem in which the task is to delete b nodes so as to minimize the number of node pairs that remain connected by a path of length at most k . With the techniques developed in this paper, instances with up to 17,000 nodes can be solved exactly. We introduce two integer programming formulations for this problem (thin and path-like) and compare them with an existing recursive formulation. Although the thin formulation generally has an exponential number of constraints, it admits an efficient separation routine. Also helpful is a new, more general preprocessing procedure that, on average, fixes three times as many variables than before. Summary of Contribution: In this paper, we consider a distance-based variant of the critical node problem in which the task is to delete b nodes so as to minimize the number of node pairs that remain connected by a path of length at most k . This problem is motivated by applications in social networks, telecommunications, and transportation networks. In our paper, we aim to solve large-scale instances of this problem. Standard out-of-the-box approaches are unable to solve such instances, requiring new integer programming models, methodological contributions, and other computational insights. For example, we propose an algorithm for finding a maximum independent set of simplicial nodes that runs in time O(nm) that we use in a preprocessing procedure; we also prove that the separation problem associated with one of our integer programming models is NP-hard. We apply our branch-and-cut implementation to real-life networks from a variety of domains and observe speedups over previous approaches.

Suggested Citation

  • Hosseinali Salemi & Austin Buchanan, 2022. "Solving the Distance-Based Critical Node Problem," INFORMS Journal on Computing, INFORMS, vol. 34(3), pages 1309-1326, May.
  • Handle: RePEc:inm:orijoc:v:34:y:2022:i:3:p:1309-1326
    DOI: 10.1287/ijoc.2021.1136
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/ijoc.2021.1136
    Download Restriction: no

    File URL: https://libkey.io/10.1287/ijoc.2021.1136?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Clayton W. Commander & Panos M. Pardalos & Valeriy Ryabchenko & Stan Uryasev & Grigoriy Zrazhevsky, 2007. "The wireless network jamming problem," Journal of Combinatorial Optimization, Springer, vol. 14(4), pages 481-498, November.
    2. Marco Di Summa & Andrea Grosso & Marco Locatelli, 2012. "Branch and cut algorithms for detecting critical nodes in undirected graphs," Computational Optimization and Applications, Springer, vol. 53(3), pages 649-680, December.
    3. Bernardetta Addis & Roberto Aringhieri & Andrea Grosso & Pierre Hosteins, 2016. "Hybrid constructive heuristics for the critical node problem," Annals of Operations Research, Springer, vol. 238(1), pages 637-649, March.
    4. Hooshmand, F. & Mirarabrazi, F. & MirHassani, S.A., 2020. "Efficient Benders decomposition for distance-based critical node detection problem," Omega, Elsevier, vol. 93(C).
    5. Austin Buchanan & Je Sang Sung & Sergiy Butenko & Eduardo L. Pasiliao, 2015. "An Integer Programming Approach for Fault-Tolerant Connected Dominating Sets," INFORMS Journal on Computing, INFORMS, vol. 27(1), pages 178-188, February.
    6. Bernardetta Addis & Roberto Aringhieri & Andrea Grosso & Pierre Hosteins, 2016. "Hybrid constructive heuristics for the critical node problem," Annals of Operations Research, Springer, vol. 238(1), pages 637-649, March.
    7. Stephen P. Borgatti, 2006. "Identifying sets of key players in a social network," Computational and Mathematical Organization Theory, Springer, vol. 12(1), pages 21-34, April.
    8. Michele Conforti & Gérard Cornuéjols & Giacomo Zambelli, 2013. "Extended formulations in combinatorial optimization," Annals of Operations Research, Springer, vol. 204(1), pages 97-143, April.
    Full references (including those not matched with items on IDEAS)

    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. Marco Di Summa & Syed Md Omar Faruk, 2023. "Critical node/edge detection problems on trees," 4OR, Springer, vol. 21(3), pages 439-455, September.
    2. Chen, Wei & Jiang, Manrui & Jiang, Cheng & Zhang, Jun, 2020. "Critical node detection problem for complex network in undirected weighted networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 538(C).
    3. Zhou, Yangming & Wang, Gezi & Hao, Jin-Kao & Geng, Na & Jiang, Zhibin, 2023. "A fast tri-individual memetic search approach for the distance-based critical node problem," European Journal of Operational Research, Elsevier, vol. 308(2), pages 540-554.
    4. Ventresca, Mario & Harrison, Kyle Robert & Ombuki-Berman, Beatrice M., 2018. "The bi-objective critical node detection problem," European Journal of Operational Research, Elsevier, vol. 265(3), pages 895-908.
    5. Gokhan Karakose & Ronald G. McGarvey, 2019. "Optimal Detection of Critical Nodes: Improvements to Model Structure and Performance," Networks and Spatial Economics, Springer, vol. 19(1), pages 1-26, March.
    6. Alexander Veremyev & Oleg A. Prokopyev & Eduardo L. Pasiliao, 2014. "An integer programming framework for critical elements detection in graphs," Journal of Combinatorial Optimization, Springer, vol. 28(1), pages 233-273, July.
    7. Li Zeng & Changjun Fan & Chao Chen, 2023. "Leveraging Minimum Nodes for Optimum Key Player Identification in Complex Networks: A Deep Reinforcement Learning Strategy with Structured Reward Shaping," Mathematics, MDPI, vol. 11(17), pages 1-13, August.
    8. Jiang, Cheng & Liu, Zhonghua, 2019. "Detecting multiple key players under the positive effect by using a distance-based connectivity approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    9. Yibo Dong & Jin Liu & Jiaqi Ren & Zhe Li & Weili Li, 2023. "Protecting Infrastructure Networks: Solving the Stackelberg Game with Interval-Valued Intuitionistic Fuzzy Number Payoffs," Mathematics, MDPI, vol. 11(24), pages 1-18, December.
    10. Joe Naoum-Sawaya & Christoph Buchheim, 2016. "Robust Critical Node Selection by Benders Decomposition," INFORMS Journal on Computing, INFORMS, vol. 28(1), pages 162-174, February.
    11. T. N. Dinh & M. T. Thai & H. T. Nguyen, 2014. "Bound and exact methods for assessing link vulnerability in complex networks," Journal of Combinatorial Optimization, Springer, vol. 28(1), pages 3-24, July.
    12. Hooshmand, F. & Mirarabrazi, F. & MirHassani, S.A., 2020. "Efficient Benders decomposition for distance-based critical node detection problem," Omega, Elsevier, vol. 93(C).
    13. Mark J. O. Bagley, 2019. "Networks, geography and the survival of the firm," Journal of Evolutionary Economics, Springer, vol. 29(4), pages 1173-1209, September.
    14. Capponi, Agostino & Corell, Felix & Stiglitz, Joseph E., 2022. "Optimal bailouts and the doom loop with a financial network," Journal of Monetary Economics, Elsevier, vol. 128(C), pages 35-50.
    15. Zhao, Shuying & Sun, Shaowei, 2023. "Identification of node centrality based on Laplacian energy of networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    16. Foad Mahdavi Pajouh, 2020. "Minimum cost edge blocker clique problem," Annals of Operations Research, Springer, vol. 294(1), pages 345-376, November.
    17. Raddant, Matthias & Takahashi, Hiroshi, 2019. "The Japanese corporate board network," Kiel Working Papers 2130, Kiel Institute for the World Economy (IfW Kiel).
    18. Liberati, Caterina & Marzo, Massimiliano & Zagaglia, Paolo & Zappa, Paola, 2012. "Structural distortions in the Euro interbank market: the role of 'key players' during the recent market turmoil," MPRA Paper 40223, University Library of Munich, Germany.
    19. Michel Grabisch & Agnieszka Rusinowska, 2015. "Lattices in Social Networks with Influence," International Game Theory Review (IGTR), World Scientific Publishing Co. Pte. Ltd., vol. 17(01), pages 1-18.
    20. Andrea Galeotti & Benjamin Golub & Sanjeev Goyal, 2020. "Targeting Interventions in Networks," Econometrica, Econometric Society, vol. 88(6), pages 2445-2471, November.

    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:inm:orijoc:v:34:y:2022:i:3:p:1309-1326. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.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.