IDEAS home Printed from https://ideas.repec.org/a/spr/jcomop/v40y2020i1d10.1007_s10878-020-00580-6.html
   My bibliography  Save this article

KATZ centrality with biogeography-based optimization for influence maximization problem

Author

Listed:
  • Abbas Salehi

    (Islamic Azad University)

  • Behrooz Masoumi

    (Islamic Azad University)

Abstract

In the field of social networks, the Influence Maximization Problem (IMP) is one of the most well-known issues that have attracted many researchers in recent years. Influence Maximization (IM) means trying to find the best subset of K nodes that maximizes the number of nodes influenced by this subset. The IM is an NP-hard problem that plays an important role in viral marketing and dissemination of information. The existing solutions like greedy approaches to solving IMP do not have the efficiency and accuracy in solving the problem. In this paper, we propose a new metaheuristic algorithm based on Katz centrality with biogeography-based optimization to solve IMP in the social network. In the proposed algorithm, each habitat with the subset of K nodes is considered as the solution to the IM problem. In the proposed algorithm, the Katz centrality of each node is calculated and used as the emigration rate of each habitat. The focus of the study has been on improving the performance of the BBO algorithm by combining it with the Katz centrality. The objective was to use an enhanced meta-heuristic algorithm with measuring centrality to solve the IM problem. In the results of experiments based on different types of real-world social networks, it is well known that the proposed algorithm is more efficient, accurate, and faster than influence maximization greedy approaches.

Suggested Citation

  • Abbas Salehi & Behrooz Masoumi, 2020. "KATZ centrality with biogeography-based optimization for influence maximization problem," Journal of Combinatorial Optimization, Springer, vol. 40(1), pages 205-226, July.
    • Handle: RePEc:spr:jcomop:v:40:y:2020:i:1:d:10.1007_s10878-020-00580-6
    DOI: 10.1007/s10878-020-00580-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10878-020-00580-6
    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/s10878-020-00580-6?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. Shang, Jiaxing & Wu, Hongchun & Zhou, Shangbo & Zhong, Jiang & Feng, Yong & Qiang, Baohua, 2018. "IMPC: Influence maximization based on multi-neighbor potential in community networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1085-1103.
    2. Weili Wu & Hongwei Du & Huijuan Wang & Lidong Wu & Zhenhua Duan & Cong Tian, 2018. "On general threshold and general cascade models of social influence," Journal of Combinatorial Optimization, Springer, vol. 35(1), pages 209-215, January.
    3. Yuqing Zhu & Weili Wu & Yuanjun Bi & Lidong Wu & Yiwei Jiang & Wen Xu, 2015. "Better approximation algorithms for influence maximization in online social networks," Journal of Combinatorial Optimization, Springer, vol. 30(1), pages 97-108, July.
    4. Tang, Jianxin & Zhang, Ruisheng & Yao, Yabing & Yang, Fan & Zhao, Zhili & Hu, Rongjing & Yuan, Yongna, 2019. "Identification of top-k influential nodes based on enhanced discrete particle swarm optimization for influence maximization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 477-496.
    5. Leo Katz, 1953. "A new status index derived from sociometric analysis," Psychometrika, Springer;The Psychometric Society, vol. 18(1), pages 39-43, March.
    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. Thomas J. Sargent & John Stachurski, 2022. "Economic Networks: Theory and Computation," Papers 2203.11972, arXiv.org, revised Jul 2022.
    2. Karimi, Fatemeh & Lotfi, Shahriar & Izadkhah, Habib, 2021. "Community-guided link prediction in multiplex networks," Journal of Informetrics, Elsevier, vol. 15(4).
    3. D’Errico, Marco & Battiston, Stefano & Peltonen, Tuomas & Scheicher, Martin, 2018. "How does risk flow in the credit default swap market?," Journal of Financial Stability, Elsevier, vol. 35(C), pages 53-74.
    4. Liu, Xiaodong & Patacchini, Eleonora & Zenou, Yves & Lee, Lung-Fei, 2011. "Criminal Networks: Who is the Key Player?," Research Papers in Economics 2011:7, Stockholm University, Department of Economics.
    5. Agnieszka Rusinowska & Rudolf Berghammer & Harrie de Swart & Michel Grabisch, 2011. "Social networks: Prestige, centrality, and influence (Invited paper)," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-00633859, HAL.
    6. Jiang, Jianhua & Yang, Xi & Meng, Xianqiu & Li, Keqin, 2020. "Enhance chaotic gravitational search algorithm (CGSA) by balance adjustment mechanism and sine randomness function for continuous optimization problems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    7. Gabrielle Demange, 2018. "Contagion in Financial Networks: A Threat Index," Management Science, INFORMS, vol. 64(2), pages 955-970, February.
    8. Lin, Dan & Wu, Jiajing & Xuan, Qi & Tse, Chi K., 2022. "Ethereum transaction tracking: Inferring evolution of transaction networks via link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    9. Yao Hongxing & Lu Yunxia, 2017. "Analyzing the Potential Influence of Shanghai Stock Market Based on Link Prediction Method," Journal of Systems Science and Information, De Gruyter, vol. 5(5), pages 446-461, October.
    10. Zhepeng Li & Xiao Fang & Xue Bai & Olivia R. Liu Sheng, 2017. "Utility-Based Link Recommendation for Online Social Networks," Management Science, INFORMS, vol. 63(6), pages 1938-1952, June.
    11. Sheikhahmadi, Amir & Nematbakhsh, Mohammad Ali & Shokrollahi, Arman, 2015. "Improving detection of influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 833-845.
    12. Dequiedt, Vianney & Zenou, Yves, 2017. "Local and consistent centrality measures in parameterized networks," Mathematical Social Sciences, Elsevier, vol. 88(C), pages 28-36.
    13. Mauleon, Ana & Nanumyan, Mariam & Vannetelbosch, Vincent, 2024. "Ideal efforts and consensus in a multi-layer network game," LIDAM Discussion Papers CORE 2024023, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    14. ,, 2014. "A ranking method based on handicaps," Theoretical Economics, Econometric Society, vol. 9(3), September.
    15. Celani, Alessandro & Cerchiello, Paola & Pagnottoni, Paolo, 2024. "The topological structure of panel variance decomposition networks," Journal of Financial Stability, Elsevier, vol. 71(C).
    16. Ernest Liu & Aleh Tsyvinski, 2021. "Dynamical Structure and Spectral Properties of Input-Output Networks," Working Papers 2021-13, Princeton University. Economics Department..
    17. Richard W. Carney & Travers Barclay Child, 2015. "Business Networks and Crisis Performance: Professional, Political, and Family Ties," Tinbergen Institute Discussion Papers 15-135/V, Tinbergen Institute, revised 20 Feb 2015.
    18. Wu, Tao & Xian, Xingping & Zhong, Linfeng & Xiong, Xi & Stanley, H. Eugene, 2018. "Power iteration ranking via hybrid diffusion for vital nodes identification," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 802-815.
    19. Shenshen Bai & Longjie Li & Jianjun Cheng & Shijin Xu & Xiaoyun Chen, 2018. "Predicting Missing Links Based on a New Triangle Structure," Complexity, Hindawi, vol. 2018, pages 1-11, December.
    20. 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.

    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:40:y:2020:i:1:d:10.1007_s10878-020-00580-6. 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.