IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v395y2014icp549-559.html
   My bibliography  Save this article

Identifying and ranking influential spreaders in complex networks by neighborhood coreness

Author

Listed:
  • Bae, Joonhyun
  • Kim, Sangwook

Abstract

Identifying influential spreaders is an important issue in understanding the dynamics of information diffusion in complex networks. The k-shell index, which is the topological location of a node in a network, is a more efficient measure at capturing the spreading ability of a node than are the degree and betweenness centralities. However, the k-shell decomposition fails to yield the monotonic ranking of spreaders because it assigns too many nodes with the same k-shell index. In this paper, we propose a novel measure, coreness centrality, to estimate the spreading influence of a node in a network using the k-shell indices of its neighbors. Our experimental results on both real and artificial networks, compared with an epidemic spreading model, show that the proposed method can quantify the node influence more accurately and provide a more monotonic ranking list than other ranking methods.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:phsmap:v:395:y:2014:i:c:p:549-559
    DOI: 10.1016/j.physa.2013.10.047
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437113010406
    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.2013.10.047?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. Gert Sabidussi, 1966. "The centrality index of a graph," Psychometrika, Springer;The Psychometric Society, vol. 31(4), pages 581-603, December.
    2. Liu, Jian-Guo & Ren, Zhuo-Ming & Guo, Qiang, 2013. "Ranking the spreading influence in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(18), pages 4154-4159.
    3. 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.
    4. Réka Albert & Hawoong Jeong & Albert-László Barabási, 2000. "Error and attack tolerance of complex networks," Nature, Nature, vol. 406(6794), pages 378-382, July.
    5. 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.
    6. Hou, Bonan & Yao, Yiping & Liao, Dongsheng, 2012. "Identifying all-around nodes for spreading dynamics in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(15), pages 4012-4017.
    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. 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.
    2. 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.
    3. Hu, Jiantao & Du, Yuxian & Mo, Hongming & Wei, Daijun & Deng, Yong, 2016. "A modified weighted TOPSIS to identify influential nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 73-85.
    4. Mahyar, Hamidreza & Hasheminezhad, Rouzbeh & Ghalebi K., Elahe & Nazemian, Ali & Grosu, Radu & Movaghar, Ali & Rabiee, Hamid R., 2018. "Compressive sensing of high betweenness centrality nodes in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 497(C), pages 166-184.
    5. 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.
    6. Gao, Shuai & Ma, Jun & Chen, Zhumin & Wang, Guanghui & Xing, Changming, 2014. "Ranking the spreading ability of nodes in complex networks based on local structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 403(C), pages 130-147.
    7. 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).
    8. Zareie, Ahmad & Sheikhahmadi, Amir & Fatemi, Adel, 2017. "Influential nodes ranking in complex networks: An entropy-based approach," Chaos, Solitons & Fractals, Elsevier, vol. 104(C), pages 485-494.
    9. 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.
    10. 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).
    11. Wei, Bo & Liu, Jie & Wei, Daijun & Gao, Cai & Deng, Yong, 2015. "Weighted k-shell decomposition for complex networks based on potential edge weights," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 420(C), pages 277-283.
    12. 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.
    13. Zhe Li & Xinyu Huang, 2023. "Identifying Influential Spreaders Using Local Information," Mathematics, MDPI, vol. 11(6), pages 1-14, March.
    14. Yu, Senbin & Gao, Liang & Xu, Lida & Gao, Zi-You, 2019. "Identifying influential spreaders based on indirect spreading in neighborhood," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 418-425.
    15. Wu, Yali & Dong, Ang & Ren, Yuanguang & Jiang, Qiaoyong, 2023. "Identify influential nodes in complex networks: A k-orders entropy-based method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P1).
    16. 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.
    17. Fu, Yu-Hsiang & Huang, Chung-Yuan & Sun, Chuen-Tsai, 2015. "Using global diversity and local topology features to identify influential network spreaders," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 433(C), pages 344-355.
    18. 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.
    19. Liu, Jun & Xiong, Qingyu & Shi, Weiren & Shi, Xin & Wang, Kai, 2016. "Evaluating the importance of nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 209-219.
    20. Jiang, Zhong-Yuan & Zeng, Yong & Liu, Zhi-Hong & Ma, Jian-Feng, 2019. "Identifying critical nodes’ group in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 121-132.

    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:395:y:2014:i:c:p:549-559. 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.