IDEAS home Printed from https://ideas.repec.org/a/eee/jomega/v101y2021ics030504831931059x.html
   My bibliography  Save this article

Finding influential groups in networked systems: The most degree-central clique problem

Author

Listed:
  • Zhong, Haonan
  • Mahdavi Pajouh, Foad
  • Prokopyev, Oleg A.

Abstract

Degree centrality of a cluster of vertices in a network is defined as the number of vertices outside the cluster that are adjacent to at least one vertex in the cluster. The concept of degree centrality is often used in the network analysis literature to quantify the influence of the vertex cluster within the network. That is, a large value of degree centrality shows that the cluster is adjacent to a large number of vertices outside the cluster, thus indicating its high potential for directly influencing outside components in the network. In this paper, we study the most degree-central clique problem, which is defined as the problem of finding a clique of maximum degree centrality in a network. In other words, we seek an influential cohesive cluster of vertices with no restrictions on the size of the cluster, but requiring that the cluster is highly cohesive by itself, i.e., it forms a clique. We establish that the decision version of considered problem is NP-complete. Then, we explore important theoretical properties of the problem and consequently exploit them to implement a specialized combinatorial branch-and-bound algorithm. Finally, using a collection of randomly generated and real-life networks, we compare the performance of our exact algorithm against an integer programming formulation, along with the discussion of some interesting insights.

Suggested Citation

  • Zhong, Haonan & Mahdavi Pajouh, Foad & Prokopyev, Oleg A., 2021. "Finding influential groups in networked systems: The most degree-central clique problem," Omega, Elsevier, vol. 101(C).
    • Handle: RePEc:eee:jomega:v:101:y:2021:i:c:s030504831931059x
    DOI: 10.1016/j.omega.2020.102262
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030504831931059X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.omega.2020.102262?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. Yang, Jianmei & Yao, Canzhong & Ma, Weicheng & Chen, Guanrong, 2010. "A study of the spreading scheme for viral marketing based on a complex network model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(4), pages 859-870.
    2. Yezerska, Oleksandra & Mahdavi Pajouh, Foad & Butenko, Sergiy, 2017. "On biconnected and fragile subgraphs of low diameter," European Journal of Operational Research, Elsevier, vol. 263(2), pages 390-400.
    3. Rysz, Maciej & Mahdavi Pajouh, Foad & Pasiliao, Eduardo L., 2018. "Finding clique clusters with the highest betweenness centrality," European Journal of Operational Research, Elsevier, vol. 271(1), pages 155-164.
    4. R. Luce & Albert Perry, 1949. "A method of matrix analysis of group structure," Psychometrika, Springer;The Psychometric Society, vol. 14(2), pages 95-116, June.
    5. 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. Camur, Mustafa C. & Sharkey, Thomas C. & Vogiatzis, Chrysafis, 2023. "The stochastic pseudo-star degree centrality problem," European Journal of Operational Research, Elsevier, vol. 308(2), pages 525-539.
    2. Matsypura, Dmytro & Veremyev, Alexander & Pasiliao, Eduardo L. & Prokopyev, Oleg A., 2023. "Finding the most degree-central walks and paths in a graph: Exact and heuristic approaches," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1021-1036.
    3. Wen, Tao & Chen, Yu-wang & Syed, Tahir abbas & Wu, Ting, 2024. "ERIUE: Evidential reasoning-based influential users evaluation in social networks," Omega, Elsevier, vol. 122(C).
    4. Huang, Wencheng & Li, Haoran & Yin, Yanhui & Zhang, Zhi & Xie, Anhao & Zhang, Yin & Cheng, Guo, 2024. "Node importance identification of unweighted urban rail transit network: An Adjacency Information Entropy based approach," Reliability Engineering and System Safety, Elsevier, vol. 242(C).

    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. 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.
    2. 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.
    3. Nasirian, Farzaneh & Mahdavi Pajouh, Foad & Balasundaram, Balabhaskar, 2020. "Detecting a most closeness-central clique in complex networks," European Journal of Operational Research, Elsevier, vol. 283(2), pages 461-475.
    4. 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.
    5. 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.
    6. 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.
    7. Matsypura, Dmytro & Veremyev, Alexander & Pasiliao, Eduardo L. & Prokopyev, Oleg A., 2023. "Finding the most degree-central walks and paths in a graph: Exact and heuristic approaches," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1021-1036.
    8. Oleksandra Yezerska & Sergiy Butenko & Vladimir L. Boginski, 2018. "Detecting robust cliques in graphs subject to uncertain edge failures," Annals of Operations Research, Springer, vol. 262(1), pages 109-132, March.
    9. Foad Mahdavi Pajouh & Balabhaskar Balasundaram & Illya V. Hicks, 2016. "On the 2-Club Polytope of Graphs," Operations Research, INFORMS, vol. 64(6), pages 1466-1481, December.
    10. Yezerska, Oleksandra & Mahdavi Pajouh, Foad & Butenko, Sergiy, 2017. "On biconnected and fragile subgraphs of low diameter," European Journal of Operational Research, Elsevier, vol. 263(2), pages 390-400.
    11. Vladimir Boginski & Sergiy Butenko & Oleg Shirokikh & Svyatoslav Trukhanov & Jaime Gil Lafuente, 2014. "A network-based data mining approach to portfolio selection via weighted clique relaxations," Annals of Operations Research, Springer, vol. 216(1), pages 23-34, May.
    12. 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.
    13. Yajun Lu & Hosseinali Salemi & Balabhaskar Balasundaram & Austin Buchanan, 2022. "On Fault-Tolerant Low-Diameter Clusters in Graphs," INFORMS Journal on Computing, INFORMS, vol. 34(6), pages 3181-3199, November.
    14. Veremyev, Alexander & Prokopyev, Oleg A. & Boginski, Vladimir & Pasiliao, Eduardo L., 2014. "Finding maximum subgraphs with relatively large vertex connectivity," European Journal of Operational Research, Elsevier, vol. 239(2), pages 349-362.
    15. Noah E. Friedkin, 1984. "Structural Cohesion and Equivalence Explanations of Social Homogeneity," Sociological Methods & Research, , vol. 12(3), pages 235-261, February.
    16. Sheng Bin, 2023. "Social Network Emotional Marketing Influence Model of Consumers’ Purchase Behavior," Sustainability, MDPI, vol. 15(6), pages 1-17, March.
    17. Foad Mahdavi Pajouh, 2020. "Minimum cost edge blocker clique problem," Annals of Operations Research, Springer, vol. 294(1), pages 345-376, November.
    18. Zhu, Yongjun & Yan, Erjia, 2017. "Examining academic ranking and inequality in library and information science through faculty hiring networks," Journal of Informetrics, Elsevier, vol. 11(2), pages 641-654.
    19. 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.
    20. 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.

    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:eee:jomega:v:101:y:2021:i:c:s030504831931059x. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/375/description#description .

    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.