IDEAS home Printed from https://ideas.repec.org/a/spr/jcomop/v28y2014i3d10.1007_s10878-013-9687-8.html
   My bibliography  Save this article

Computing an effective decision making group of a society using social network analysis

Author

Listed:
  • Donghyun Kim

    (North Carolina Central University)

  • Deying Li

    (Renmin University of China)

  • Omid Asgari

    (North Carolina Central University)

  • Yingshu Li

    (Georgia State University)

  • Alade O. Tokuta

    (North Carolina Central University)

  • Heekuck Oh

    (Hanyang University)

Abstract

Recent years have witnessed how much a decision making group can be dysfunctional due to the extreme hyperpartisanship. While partisanship is crucial for the representatives to pursue the wishes of those whom they represent for, such an extremism results in a severe gridlock in the decision making progress, and makes themselves highly inefficient. It is known that such a problem can be mitigated by having negotiators in the group. This paper investigates the potential of social network analysis techniques to choose an effective leadership group of a society such that it suffers less from the extreme hyperpartisanship. We establish three essential requirements for an effective representative group, namely Influenceability, Partisanship, and Bipartisanship. Then, we formulate the problem of finding a minimum size representative group satisfying the three requirements as the minimum connected $$k$$ k -core dominating set problem (MC $$k$$ k CDSP), and show its NP-hardness. We introduce an extension of MC $$k$$ k CDSP, namely MC $$k$$ k CDSP-C, which assumes the society has a number of sub-communities and requires at least one representative from each sub-community should be in the leadership. We also propose an approximation algorithm for a subclass of MC $$k$$ k CDSP with $$k=2$$ k = 2 , and show an $$\alpha $$ α -approximation algorithm of MC $$k$$ k CDSP can be used to obtain an $$\alpha $$ α -approximation algorithm of MC $$k$$ k CDSP-SC.

Suggested Citation

  • Donghyun Kim & Deying Li & Omid Asgari & Yingshu Li & Alade O. Tokuta & Heekuck Oh, 2014. "Computing an effective decision making group of a society using social network analysis," Journal of Combinatorial Optimization, Springer, vol. 28(3), pages 577-587, October.
  • Handle: RePEc:spr:jcomop:v:28:y:2014:i:3:d:10.1007_s10878-013-9687-8
    DOI: 10.1007/s10878-013-9687-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10878-013-9687-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10878-013-9687-8?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kelleher, Laura L. & Cozzens, Margaret B., 1988. "Dominating sets in social network graphs," Mathematical Social Sciences, Elsevier, vol. 16(3), pages 267-279, December.
    2. Xu Zhu & Jieun Yu & Wonjun Lee & Donghyun Kim & Shan Shan & Ding-Zhu Du, 2010. "New dominating sets in social networks," Journal of Global Optimization, Springer, vol. 48(4), pages 633-642, December.
    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. Laetitia Legalais, 2015. "L'Influence D'Un Blocage De Carriere Sur La Construction De L'Identite Professionnelle : Le Cas Des Contrôleurs De Gestion," Post-Print hal-01188764, HAL.
    2. Thang N. Dinh & Yilin Shen & Dung T. Nguyen & My T. Thai, 2014. "On the approximability of positive influence dominating set in social networks," Journal of Combinatorial Optimization, Springer, vol. 27(3), pages 487-503, April.
    3. S. Raghavan & Rui Zhang, 2022. "Rapid Influence Maximization on Social Networks: The Positive Influence Dominating Set Problem," INFORMS Journal on Computing, INFORMS, vol. 34(3), pages 1345-1365, May.
    4. Lin, Geng & Guan, Jian & Feng, Huibin, 2018. "An ILP based memetic algorithm for finding minimum positive influence dominating sets in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 500(C), pages 199-209.
    5. S. Raghavan & Rui Zhang, 2022. "Influence Maximization with Latency Requirements on Social Networks," INFORMS Journal on Computing, INFORMS, vol. 34(2), pages 710-728, March.
    6. Mahdavi Pajouh, Foad & Walteros, Jose L. & Boginski, Vladimir & Pasiliao, Eduardo L., 2015. "Minimum edge blocker dominating set problem," European Journal of Operational Research, Elsevier, vol. 247(1), pages 16-26.
    7. Emiliano Alvarez & Juan Gabriel Brida & Pablo Mones, 2024. "On the Dynamics of Relative Prices and the Relationship with Inflation: An Empirical Approach," Computational Economics, Springer;Society for Computational Economics, vol. 63(1), pages 339-355, January.
    8. Balasundaram, Balabhaskar & Borrero, Juan S. & Pan, Hao, 2022. "Graph signatures: Identification and optimization," European Journal of Operational Research, Elsevier, vol. 296(3), pages 764-775.
    9. Ruizhi Li & Shuli Hu & Huan Liu & Ruiting Li & Dantong Ouyang & Minghao Yin, 2019. "Multi-Start Local Search Algorithm for the Minimum Connected Dominating Set Problems," Mathematics, MDPI, vol. 7(12), pages 1-14, December.
    10. Wenguo Yang & Yapu Zhang & Ding-Zhu Du, 2020. "Influence maximization problem: properties and algorithms," Journal of Combinatorial Optimization, Springer, vol. 40(4), pages 907-928, November.
    11. Adeline Gilson, 2012. "La mesure du travail opérée par les salariés : une norme partagée ?," Post-Print halshs-00659859, HAL.
    12. Yijing Wang & Dachuan Xu & Yishui Wang & Dongmei Zhang, 2020. "Non-submodular maximization on massive data streams," Journal of Global Optimization, Springer, vol. 76(4), pages 729-743, April.

    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:spr:jcomop:v:28:y:2014:i:3:d:10.1007_s10878-013-9687-8. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.