IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0066732.html
   My bibliography  Save this article

A Bio-Inspired Methodology of Identifying Influential Nodes in Complex Networks

Author

Listed:
  • Cai Gao
  • Xin Lan
  • Xiaoge Zhang
  • Yong Deng

Abstract

How to identify influential nodes is a key issue in complex networks. The degree centrality is simple, but is incapable to reflect the global characteristics of networks. Betweenness centrality and closeness centrality do not consider the location of nodes in the networks, and semi-local centrality, leaderRank and pageRank approaches can be only applied in unweighted networks. In this paper, a bio-inspired centrality measure model is proposed, which combines the Physarum centrality with the K-shell index obtained by K-shell decomposition analysis, to identify influential nodes in weighted networks. Then, we use the Susceptible-Infected (SI) model to evaluate the performance. Examples and applications are given to demonstrate the adaptivity and efficiency of the proposed method. In addition, the results are compared with existing methods.

Suggested Citation

  • Cai Gao & Xin Lan & Xiaoge Zhang & Yong Deng, 2013. "A Bio-Inspired Methodology of Identifying Influential Nodes in Complex Networks," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-11, June.
  • Handle: RePEc:plo:pone00:0066732
    DOI: 10.1371/journal.pone.0066732
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0066732
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0066732&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0066732?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
    ---><---

    References listed on IDEAS

    as
    1. Jordán, Ferenc & Benedek, Zsófia & Podani, János, 2007. "Quantifying positional importance in food webs: A comparison of centrality indices," Ecological Modelling, Elsevier, vol. 205(1), pages 270-275.
    2. Wang, Jia-zeng & Liu, Zeng-rong & Xu, Jianhua, 2007. "Epidemic spreading on uncorrelated heterogenous networks with non-uniform transmission," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 382(2), pages 715-721.
    3. Gao, Ya-Chun & Cai, Shi-Min & Lü, Linyuan & Wang, Bing-Hong, 2013. "Evolutionary model on market ecology of investors and investments," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(16), pages 3385-3391.
    4. Enrico Zio & Giovanni Sansavini, 2011. "Component Criticality in Failure Cascade Processes of Network Systems," Risk Analysis, John Wiley & Sons, vol. 31(8), pages 1196-1210, August.
    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. Wang, Fahui & Antipova, Anzhelika & Porta, Sergio, 2011. "Street centrality and land use intensity in Baton Rouge, Louisiana," Journal of Transport Geography, Elsevier, vol. 19(2), pages 285-293.
    7. Wei, Daijun & Deng, Xinyang & Zhang, Xiaoge & Deng, Yong & Mahadevan, Sankaran, 2013. "Identifying influential nodes in weighted networks based on evidence theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(10), pages 2564-2575.
    8. Linyuan Lü & Yi-Cheng Zhang & Chi Ho Yeung & Tao Zhou, 2011. "Leaders in Social Networks, the Delicious Case," PLOS ONE, Public Library of Science, vol. 6(6), pages 1-9, June.
    9. Naoki Masuda & Issei Kurahashi & Hiroko Onari, 2013. "Suicide Ideation of Individuals in Online Social Networks," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-8, April.
    10. Leland H. Hartwell & John J. Hopfield & Stanislas Leibler & Andrew W. Murray, 1999. "From molecular to modular cell biology," Nature, Nature, vol. 402(6761), pages 47-52, December.
    11. Tao Zhou & Matúš Medo & Giulio Cimini & Zi-Ke Zhang & Yi-Cheng Zhang, 2011. "Emergence of Scale-Free Leadership Structure in Social Recommender Systems," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-6, July.
    12. Tero, Atsushi & Kobayashi, Ryo & Nakagaki, Toshiyuki, 2006. "Physarum solver: A biologically inspired method of road-network navigation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 363(1), pages 115-119.
    13. 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)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Yuping Jin & Yanbin Yang & Wei Liu, 2022. "Finding Global Liquefied Natural Gas Potential Trade Relations Based on Improved Link Prediction," Sustainability, MDPI, vol. 14(19), pages 1-22, September.
    3. Chaharborj, Sarkhosh Seddighi & Nabi, Khondoker Nazmoon & Feng, Koo Lee & Chaharborj, Shahriar Seddighi & Phang, Pei See, 2022. "Controlling COVID-19 transmission with isolation of influential nodes," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    4. Gao, Cai & Yan, Chao & Zhang, Zili & Hu, Yong & Mahadevan, Sankaran & Deng, Yong, 2014. "An amoeboid algorithm for solving linear transportation problem," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 398(C), pages 179-186.
    5. Wang, Feifei & Sun, Zejun & Gan, Quan & Fan, Aiwan & Shi, Hesheng & Hu, Haifeng, 2022. "Influential node identification by aggregating local structure information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    6. Curado, Manuel & Rodriguez, Rocio & Tortosa, Leandro & Vicent, Jose F., 2022. "Anew centrality measure in dense networks based on two-way random walk betweenness," Applied Mathematics and Computation, Elsevier, vol. 412(C).
    7. Fei, Liguo & Deng, Yong, 2017. "A new method to identify influential nodes based on relative entropy," Chaos, Solitons & Fractals, Elsevier, vol. 104(C), pages 257-267.
    8. 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).
    9. Bian, Tian & Hu, Jiantao & Deng, Yong, 2017. "Identifying influential nodes in complex networks based on AHP," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 422-436.
    10. 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).
    11. Zhang, Xiaohong & Li, Zhiying & Qian, Kai & Ren, Jianji & Luo, Junwei, 2020. "Influential node identification in a constrained greedy way," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    12. Ahmad, Amreen & Ahmad, Tanvir & Bhatt, Abhishek, 2020. "HWSMCB: A community-based hybrid approach for identifying influential nodes in the social network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    13. Wang, Zhi-Yong & Zhang, Cui-Ping & Othman Yahya, Rebaz, 2024. "High-quality community detection in complex networks based on node influence analysis," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    14. Liu, Jie & Li, Yunpeng & Ruan, Zichan & Fu, Guangyuan & Chen, Xiaowu & Sadiq, Rehan & Deng, Yong, 2015. "A new method to construct co-author networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 29-39.
    15. Gao, Cai & Wei, Daijun & Hu, Yong & Mahadevan, Sankaran & Deng, Yong, 2013. "A modified evidential methodology of identifying influential nodes in weighted networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(21), pages 5490-5500.
    16. Tuğal, İhsan & Karcı, Ali, 2019. "Comparisons of Karcı and Shannon entropies and their effects on centrality of social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 352-363.
    17. Du, Yuxian & Gao, Cai & Hu, Yong & Mahadevan, Sankaran & Deng, Yong, 2014. "A new method of identifying influential nodes in complex networks based on TOPSIS," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 399(C), pages 57-69.

    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. Gao, Cai & Wei, Daijun & Hu, Yong & Mahadevan, Sankaran & Deng, Yong, 2013. "A modified evidential methodology of identifying influential nodes in weighted networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(21), pages 5490-5500.
    2. Duan-Bing Chen & Hui Gao & Linyuan Lü & Tao Zhou, 2013. "Identifying Influential Nodes in Large-Scale Directed Networks: The Role of Clustering," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-10, October.
    3. 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.
    4. 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).
    5. 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.
    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. Zhai, Li & Yan, Xiangbin & Zhang, Guojing, 2018. "Bi-directional h-index: A new measure of node centrality in weighted and directed networks," Journal of Informetrics, Elsevier, vol. 12(1), pages 299-314.
    8. 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.
    9. Wang, Xiaojie & Zhang, Xue & Zhao, Chengli & Yi, Dongyun, 2018. "Effectively identifying multiple influential spreaders in term of the backward–forward propagation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 404-413.
    10. 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.
    11. Gao, Cai & Yan, Chao & Zhang, Zili & Hu, Yong & Mahadevan, Sankaran & Deng, Yong, 2014. "An amoeboid algorithm for solving linear transportation problem," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 398(C), pages 179-186.
    12. Chen, Yahong & Li, Jinlin & Huang, He & Ran, Lun & Hu, Yusheng, 2017. "Encouraging information sharing to boost the name-your-own-price auction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 108-117.
    13. Zhou, Ming-Yang & Xiong, Wen-Man & Wu, Xiang-Yang & Zhang, Yu-Xia & Liao, Hao, 2018. "Overlapping influence inspires the selection of multiple spreaders in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 76-83.
    14. Yingzhi Zhang & Shubin Liang & Jialin Liu & Peilong Cao & Lan Luan, 2021. "Evaluation for machine tool components importance based on improved LeaderRank," Journal of Risk and Reliability, , vol. 235(3), pages 331-337, June.
    15. Wang, Jingjing & Xu, Shuqi & Mariani, Manuel S. & Lü, Linyuan, 2021. "The local structure of citation networks uncovers expert-selected milestone papers," Journal of Informetrics, Elsevier, vol. 15(4).
    16. 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.
    17. Shugang Li & Ziming Wang & Beiyan Zhang & Boyi Zhu & Zhifang Wen & Zhaoxu Yu, 2022. "The Research of “Products Rapidly Attracting Users” Based on the Fully Integrated Link Prediction Algorithm," Mathematics, MDPI, vol. 10(14), pages 1-19, July.
    18. 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.
    19. Salavati, Chiman & Abdollahpouri, Alireza & Manbari, Zhaleh, 2018. "BridgeRank: A novel fast centrality measure based on local structure of the network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 635-653.
    20. Zhang, Yin-Ting & Zhou, Wei-Xing, 2023. "Quantifying the status of economies in international crop trade networks: A correlation structure analysis of various node-ranking metrics," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0066732. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.