IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v513y2019icp297-306.html
   My bibliography  Save this article

Identification of influential spreaders in bipartite networks:A singular value decomposition approach

Author

Listed:
  • Xu, Shuang
  • Wang, Pei
  • Zhang, Chunxia

Abstract

A bipartite network is a graph that contains two disjoint sets of nodes, such that every edge connects the two node sets. The significance of identifying influential nodes in bipartite networks is highlighted from both theoretical and practical perspectives. By considering the unique feature of bipartite networks, namely, links between the same node set are forbidden, we propose two new algorithms, called SVD-rank and SVDA-rank respectively. In the two algorithms, singular value decomposition (SVD) is performed on the original bipartite network and augmented network (two ground nodes are added). Susceptible–Infected–Recovered (SIR) model is employed to evaluate the performance of the two algorithms. Simulations on seven real-world networks show that the proposed algorithms can well identify influential spreaders in bipartite networks, and the two algorithms are robust to network perturbations. The proposed algorithms may have potential applications in the control of bipartite networks.

Suggested Citation

  • Xu, Shuang & Wang, Pei & Zhang, Chunxia, 2019. "Identification of influential spreaders in bipartite networks:A singular value decomposition approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 297-306.
    • Handle: RePEc:eee:phsmap:v:513:y:2019:i:c:p:297-306
    DOI: 10.1016/j.physa.2018.09.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437118311294
    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.2018.09.005?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. 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.
    2. Zhang, Peng & Wang, Jinliang & Li, Xiaojia & Li, Menghui & Di, Zengru & Fan, Ying, 2008. "Clustering coefficient and community structure of bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(27), pages 6869-6875.
    3. 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.
    4. Li, Qian & Zhou, Tao & Lü, Linyuan & Chen, Duanbing, 2014. "Identifying influential spreaders by weighted LeaderRank," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 404(C), pages 47-55.
    5. Flaviano Morone & Hernán A. Makse, 2015. "Influence maximization in complex networks through optimal percolation," Nature, Nature, vol. 524(7563), pages 65-68, August.
    6. Linyuan Lü & Tao Zhou & Qian-Ming Zhang & H. Eugene Stanley, 2016. "The H-index of a network node and its relation to degree and coreness," Nature Communications, Nature, vol. 7(1), pages 1-7, April.
    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. Tripathi, Richa & Reza, Amit, 2020. "A subset selection based approach to structural reducibility of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(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. 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.
    2. Zhang, Jun-li & Fu, Yan-jun & Cheng, Lan & Yang, Yun-yun, 2021. "Identifying multiple influential spreaders based on maximum connected component decomposition method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 571(C).
    3. 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.
    4. 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.
    5. Kang, Xinyu & Wang, Minxi & Chen, Lu & Li, Xin, 2023. "Supply risk propagation of global copper industry chain based on multi-layer complex network," Resources Policy, Elsevier, vol. 85(PA).
    6. Xu, Shuqi & Mariani, Manuel Sebastian & Lü, Linyuan & Medo, Matúš, 2020. "Unbiased evaluation of ranking metrics reveals consistent performance in science and technology citation data," Journal of Informetrics, Elsevier, vol. 14(1).
    7. Sheikhahmadi, Amir & Nematbakhsh, Mohammad Ali & Zareie, Ahmad, 2017. "Identification of influential users by neighbors in online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 517-534.
    8. Zhong, Lin-Feng & Shang, Ming-Sheng & Chen, Xiao-Long & Cai, Shi-Ming, 2018. "Identifying the influential nodes via eigen-centrality from the differences and similarities of structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 77-82.
    9. Bao, Zhong-Kui & Ma, Chuang & Xiang, Bing-Bing & Zhang, Hai-Feng, 2017. "Identification of influential nodes in complex networks: Method from spreading probability viewpoint," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 391-397.
    10. Wu, Rui-Jie & Kong, Yi-Xiu & Di, Zengru & Zhang, Yi-Cheng & Shi, Gui-Yuan, 2022. "Analytical solution to the k-core pruning process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    11. Xu, Guiqiong & Meng, Lei, 2023. "A novel algorithm for identifying influential nodes in complex networks based on local propagation probability model," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    12. Zhang, Dayong & Men, Hao & Zhang, Zhaoxin, 2024. "Assessing the stability of collaboration networks: A structural cohesion analysis perspective," Journal of Informetrics, Elsevier, vol. 18(1).
    13. Sun, Peng Gang & Che, Wanping & Quan, Yining & Wang, Shuzhen & Miao, Qiguang, 2022. "Random networks are heterogeneous exhibiting a multi-scaling law," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 587(C).
    14. Sun, Hong-liang & Chen, Duan-bing & He, Jia-lin & Ch’ng, Eugene, 2019. "A voting approach to uncover multiple influential spreaders on weighted networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 303-312.
    15. Wang, Dong & Small, Michael & Zhao, Yi, 2021. "Exploring the optimal network topology for spreading dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 564(C).
    16. Wen, Tao & Jiang, Wen, 2019. "Identifying influential nodes based on fuzzy local dimension in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 119(C), pages 332-342.
    17. Ye, Yucheng & Xu, Shuqi & Mariani, Manuel Sebastian & Lü, Linyuan, 2022. "Forecasting countries' gross domestic product from patent data," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    18. Li, Kaiwen & Liu, Kai & Wang, Ming, 2021. "Robustness of the Chinese power grid to cascading failures under attack and defense strategies," International Journal of Critical Infrastructure Protection, Elsevier, vol. 33(C).
    19. Zhong, Lin-Feng & Liu, Quan-Hui & Wang, Wei & Cai, Shi-Min, 2018. "Comprehensive influence of local and global characteristics on identifying the influential nodes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 511(C), pages 78-84.
    20. Liu, Qipeng & Hong, Tao, 2018. "Sequential seeding for spreading in complex networks: Influence of the network topology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 10-17.

    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:513:y:2019:i:c:p:297-306. 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.