IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v119y2019icp332-342.html
   My bibliography  Save this article

Identifying influential nodes based on fuzzy local dimension in complex networks

Author

Listed:
  • Wen, Tao
  • Jiang, Wen

Abstract

How to identify influential nodes in complex networks is an important aspect in the study of complex network. In this paper, a novel fuzzy local dimension (FLD) is proposed to rank the influential nodes in complex networks, where a node with high fuzzy local dimension has high influential ability. This proposed method focuses on the influence of the distance from the center node on the local dimension of center node by fuzzy set, resulting in a change in influential ability. In order to show this proposed method’s effectiveness and accuracy, four real-world networks are applied in this paper. Meanwhile, Susceptible-Infected (SI) is used to simulate the spreading process by FLD and other centrality measures, and Kendall’s tau coefficient is used to describe the correlation between the influential nodes obtained by centrality and the results measured by SI model. Observing from the ranking lists and simulated results, this method is effective and accurate to rank the influential nodes.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:chsofr:v:119:y:2019:i:c:p:332-342
    DOI: 10.1016/j.chaos.2019.01.011
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077919300165
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2019.01.011?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. Wen, Tao & Jiang, Wen, 2018. "An information dimension of weighted complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 388-399.
    2. Clough, James R. & Evans, Tim S., 2016. "What is the dimension of citation space?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 448(C), pages 235-247.
    3. Hong, Chen & Zhang, Jun & Cao, Xian-Bin & Du, Wen-Bo, 2016. "Structural properties of the Chinese air transportation multilayer network," Chaos, Solitons & Fractals, Elsevier, vol. 86(C), pages 28-34.
    4. Duan, Shuyu & Wen, Tao & Jiang, Wen, 2019. "A new information dimension of complex network based on Rényi entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 529-542.
    5. Huang, Zhiming & Yang, Lin & Jiang, Wen, 2019. "Uncertainty measurement with belief entropy on the interference effect in the quantum-like Bayesian Networks," Applied Mathematics and Computation, Elsevier, vol. 347(C), pages 417-428.
    6. 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.
    7. 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.
    8. 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.
    9. Li, Meizhu & Zhang, Qi & Deng, Yong, 2018. "Evidential identification of influential nodes in network of networks," Chaos, Solitons & Fractals, Elsevier, vol. 117(C), pages 283-296.
    10. Jianxi Gao & Baruch Barzel & Albert-László Barabási, 2016. "Universal resilience patterns in complex networks," Nature, Nature, vol. 530(7590), pages 307-312, February.
    11. Wan, Yi-Ping & Wang, Jian & Zhang, Dong-Ge & Dong, Hao-Yang & Ren, Qing-Hui, 2018. "Ranking the spreading capability of nodes in complex networks based on link significance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 929-937.
    12. 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.
    13. Liu, Ying & Tang, Ming & Zhou, Tao & Do, Younghae, 2016. "Identify influential spreaders in complex networks, the role of neighborhood," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 289-298.
    14. Deng, Xinyang & Jiang, Wen & Wang, Zhen, 2019. "Zero-sum polymatrix games with link uncertainty: A Dempster-Shafer theory solution," Applied Mathematics and Computation, Elsevier, vol. 340(C), pages 101-112.
    15. 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. Yang, Pingle & Meng, Fanyuan & Zhao, Laijun & Zhou, Lixin, 2023. "AOGC: An improved gravity centrality based on an adaptive truncation radius and omni-channel paths for identifying key nodes in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    2. Lei, Mingli, 2022. "Information dimension based on Deng entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    3. Du, Yuxian & Lin, Xi & Pan, Ye & Chen, Zhaoxin & Xia, Huan & Luo, Qian, 2023. "Identifying influential airports in airline network based on failure risk factors with TOPSIS," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).
    4. Meng, Yangyang & Tian, Xiangliang & Li, Zhongwen & Zhou, Wei & Zhou, Zhijie & Zhong, Maohua, 2020. "Exploring node importance evolution of weighted complex networks in urban rail transit," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 558(C).
    5. Lei, Mingli & Cheong, Kang Hao, 2022. "Node influence ranking in complex networks: A local structure entropy approach," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    6. Wang, Longjian & Zhang, Shuichao & Szűcs, Gábor & Wang, Yonggang, 2024. "Identifying the critical nodes in multi-modal transportation network with a traffic demand-based computational method," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    7. 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).

    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. 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).
    2. 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.
    3. Zhao, Jie & Wang, Yunchuan & Deng, Yong, 2020. "Identifying influential nodes in complex networks from global perspective," Chaos, Solitons & Fractals, Elsevier, vol. 133(C).
    4. 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).
    5. 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).
    6. Duan, Shuyu & Wen, Tao & Jiang, Wen, 2019. "A new information dimension of complex network based on Rényi entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 529-542.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. Yang, Pingle & Meng, Fanyuan & Zhao, Laijun & Zhou, Lixin, 2023. "AOGC: An improved gravity centrality based on an adaptive truncation radius and omni-channel paths for identifying key nodes in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    12. 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.
    13. Liu, Qiang & Zhu, Yu-Xiao & Jia, Yan & Deng, Lu & Zhou, Bin & Zhu, Jun-Xing & Zou, Peng, 2018. "Leveraging local h-index to identify and rank influential spreaders in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 379-391.
    14. 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.
    15. Xiaojian Ma & Yinghong Ma, 2019. "The Local Triangle Structure Centrality Method to Rank Nodes in Networks," Complexity, Hindawi, vol. 2019, pages 1-16, January.
    16. Wu, Tao & Chen, Leiting & Zhong, Linfeng & Xian, Xingping, 2017. "Enhanced collective influence: A paradigm to optimize network disruption," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 472(C), pages 43-52.
    17. Wei, Bo & Deng, Yong, 2019. "A cluster-growing dimension of complex networks: From the view of node closeness centrality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 522(C), pages 80-87.
    18. Yu Zhang & Wen Jiang & Xinyang Deng, 2019. "Fault diagnosis method based on time domain weighted data aggregation and information fusion," International Journal of Distributed Sensor Networks, , vol. 15(9), pages 15501477198, September.
    19. 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.
    20. 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).

    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:chsofr:v:119:y:2019:i:c:p:332-342. 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: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    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.