IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v583y2021ics0378437121005641.html
   My bibliography  Save this article

Identifying influential links to control spreading of epidemics

Author

Listed:
  • Huang, Binchao
  • Yang, Jin-Xuan
  • Li, Xin

Abstract

In recent years, epidemics have been raging around the world, causing serious harm to human health, and controlling the spread of the epidemics has become a hot topic. The study of network partition is of great practical significance to control the spread of the epidemics. Network partition is an NP hard problem. Much work has focused on identifying important nodes in complex networks to achieve network partition. Similarly, understanding the importance of links in spreading dynamics in a network can provide ways to hinder or slow down ongoing dynamical phenomena like the spreading of epidemic or the diffusion of information. This paper studies the algorithms to identify influential links in complex networks. Deleting some influential links can divide the network into some components so as to prevent effectively the spreading processes in epidemics. Our algorithms can successfully identify influential links at low computational cost by utilizing the eigenvector centrality measures. The simulated results in some real-world social networks and synthetic networks show that our algorithms are effective. The number of infected individuals can be remarkable reduced by removing influential links.

Suggested Citation

  • Huang, Binchao & Yang, Jin-Xuan & Li, Xin, 2021. "Identifying influential links to control spreading of epidemics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
    • Handle: RePEc:eee:phsmap:v:583:y:2021:i:c:s0378437121005641
    DOI: 10.1016/j.physa.2021.126291
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437121005641
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2021.126291?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. Aman Ullah & Bin wang & Jinfang Sheng & Jun Long & Nasrullah Khan & Lucia Valentina Gambuzza, 2021. "Identification of Influential Nodes via Effective Distance-based Centrality Mechanism in Complex Networks," Complexity, Hindawi, vol. 2021, pages 1-16, February.
    2. Pablo M. Gleiser & Leon Danon, 2003. "Community Structure In Jazz," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 6(04), pages 565-573.
    3. Chen, Duanbing & Lü, Linyuan & Shang, Ming-Sheng & Zhang, Yi-Cheng & Zhou, Tao, 2012. "Identifying influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1777-1787.
    4. Duncan J. Watts & Steven H. Strogatz, 1998. "Collective dynamics of ‘small-world’ networks," Nature, Nature, vol. 393(6684), pages 440-442, June.
    5. Harold Hotelling, 1936. "Simplified calculation of principal components," Psychometrika, Springer;The Psychometric Society, vol. 1(1), pages 27-35, March.
    6. Croccolo, Fabrizio & Roman, H. Eduardo, 2020. "Spreading of infections on random graphs: A percolation-type model for COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    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. Chen, Wenhao & Li, Jichao & Jiang, Jiang & Chen, Gang, 2022. "Weighted interdependent network disintegration strategy based on Q-learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    2. Hector Eduardo Roman & Fabrizio Croccolo, 2021. "Spreading of Infections on Network Models: Percolation Clusters and Random Trees," Mathematics, MDPI, vol. 9(23), pages 1-22, November.

    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. Liu, Panfeng & Li, Longjie & Fang, Shiyu & Yao, Yukai, 2021. "Identifying influential nodes in social networks: A voting approach," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    2. Namtirtha, Amrita & Dutta, Animesh & Dutta, Biswanath, 2018. "Identifying influential spreaders in complex networks based on kshell hybrid method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 310-324.
    3. Wang, Zhixiao & Zhao, Ya & Xi, Jingke & Du, Changjiang, 2016. "Fast ranking influential nodes in complex networks using a k-shell iteration factor," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 171-181.
    4. Zareie, Ahmad & Sheikhahmadi, Amir, 2019. "EHC: Extended H-index Centrality measure for identification of users’ spreading influence in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 141-155.
    5. Xu, Shuang & Wang, Pei, 2017. "Identifying important nodes by adaptive LeaderRank," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 654-664.
    6. Jincheng Jiang & Jinsong Chen & Wei Tu & Chisheng Wang, 2019. "A Novel Effective Indicator of Weighted Inter-City Human Mobility Networks to Estimate Economic Development," Sustainability, MDPI, vol. 11(22), pages 1-18, November.
    7. Yeruva, Sujatha & Devi, T. & Reddy, Y. Samtha, 2016. "Selection of influential spreaders in complex networks using Pareto Shell decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 133-144.
    8. Yin, Likang & Zheng, Haoyang & Bian, Tian & Deng, Yong, 2017. "An evidential link prediction method and link predictability based on Shannon entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 699-712.
    9. Lei, Mingli, 2022. "Information dimension based on Deng entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    10. Xinyu Huang & Dongming Chen & Dongqi Wang & Tao Ren, 2020. "MINE: Identifying Top- k Vital Nodes in Complex Networks via Maximum Influential Neighbors Expansion," Mathematics, MDPI, vol. 8(9), pages 1-25, August.
    11. Zhe Li & Xinyu Huang, 2023. "Identifying Influential Spreaders Using Local Information," Mathematics, MDPI, vol. 11(6), pages 1-14, March.
    12. Li, Hanwen & Shang, Qiuyan & Deng, Yong, 2021. "A generalized gravity model for influential spreaders identification in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    13. Bae, Joonhyun & Kim, Sangwook, 2014. "Identifying and ranking influential spreaders in complex networks by neighborhood coreness," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 395(C), pages 549-559.
    14. Mishra, Shivansh & Singh, Shashank Sheshar & Kumar, Ajay & Biswas, Bhaskar, 2022. "ELP: Link prediction in social networks based on ego network perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).
    15. Fei, Liguo & Zhang, Qi & Deng, Yong, 2018. "Identifying influential nodes in complex networks based on the inverse-square law," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1044-1059.
    16. Wang, Yan & Li, Haozhan & Zhang, Ling & Zhao, Linlin & Li, Wanlan, 2022. "Identifying influential nodes in social networks: Centripetal centrality and seed exclusion approach," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    17. Ren, Baoan & Zhang, Yu & Chen, Jing & Shen, Lincheng, 2019. "Efficient network disruption under imperfect information: The sharpening effect of network reconstruction with no prior knowledge," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 196-207.
    18. Wang, Min & Li, Wanchun & Guo, Yuning & Peng, Xiaoyan & Li, Yingxiang, 2020. "Identifying influential spreaders in complex networks based on improved k-shell method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    19. Mariela González-Narváez & María José Fernández-Gómez & Susana Mendes & José-Luis Molina & Omar Ruiz-Barzola & Purificación Galindo-Villardón, 2021. "Study of Temporal Variations in Species–Environment Association through an Innovative Multivariate Method: MixSTATICO," Sustainability, MDPI, vol. 13(11), pages 1-25, May.
    20. Berahmand, Kamal & Bouyer, Asgarali & Samadi, Negin, 2018. "A new centrality measure based on the negative and positive effects of clustering coefficient for identifying influential spreaders in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 110(C), pages 41-54.

    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:phsmap:v:583:y:2021:i:c:s0378437121005641. 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.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.