IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v452y2016icp209-219.html
   My bibliography  Save this article

Evaluating the importance of nodes in complex networks

Author

Listed:
  • Liu, Jun
  • Xiong, Qingyu
  • Shi, Weiren
  • Shi, Xin
  • Wang, Kai

Abstract

Evaluating the importance of nodes for complex networks is of great significance to the research of survivability and robusticity of networks. This paper proposes an effective ranking method based on degree value and the importance of lines. It can well identify the importance of bridge nodes with lower computational complexity. Firstly, the properties of nodes that are connected to a line are used to compute the importance of the line. Then, the contribution of nodes to the importance of lines is calculated. Finally, degree of nodes and the contribution of nodes to the importance of lines are considered to rank the importance of nodes. Five real networks are used as test data. The experimental results show that our method can effectively evaluate the importance of nodes for complex networks.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:phsmap:v:452:y:2016:i:c:p:209-219
    DOI: 10.1016/j.physa.2016.02.049
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437116002156
    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.2016.02.049?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. 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.
    2. 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.
    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. R. Kinney & P. Crucitti & R. Albert & V. Latora, 2005. "Modeling cascading failures in the North American power grid," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 46(1), pages 101-107, July.
    5. 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.
    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)

    Citations

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


    Cited by:

    1. Wen, Xiangxi & Tu, Congliang & Wu, Minggong, 2018. "Node importance evaluation in aviation network based on “No Return” node deletion method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 546-559.
    2. Tajana Ban Kirigin & Sanda Bujačić Babić & Benedikt Perak, 2022. "Semi-Local Integration Measure of Node Importance," Mathematics, MDPI, vol. 10(3), pages 1-16, January.
    3. Tang, Jinjun & Li, Zhitao & Gao, Fan & Zong, Fang, 2021. "Identifying critical metro stations in multiplex network based on D–S evidence theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    4. Yu, Hui & Cao, Xi & Liu, Zun & Li, Yongjun, 2017. "Identifying key nodes based on improved structural holes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 318-327.
    5. Yupeng Li & Zhaotong Wang & Xiaoyu Zhong & Fan Zou, 2019. "Identification of influential function modules within complex products and systems based on weighted and directed complex networks," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2375-2390, August.
    6. Meisam Akbarzadeh & Soroush Memarmontazerin & Sybil Derrible & Sayed Farzin Salehi Reihani, 2019. "The role of travel demand and network centrality on the connectivity and resilience of an urban street system," Transportation, Springer, vol. 46(4), pages 1127-1141, August.
    7. Park, Ji Hwan & Chang, Woojin & Song, Jae Wook, 2020. "Link prediction in the Granger causality network of the global currency market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    8. Barroso, João Barata Ribeiro Blanco & Silva, Thiago Christiano & Souza, Sergio Rubens Stancato de, 2018. "Identifying systemic risk drivers in financial networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 650-674.
    9. Hao, Hongchang & Ma, Zhe & Wang, Anjian & Xing, Wanli & Song, Hao & Zhao, Pei & Wei, Jiangqiao & Zheng, Shuxian, 2023. "Modeling and assessing the robustness of the lithium global trade system against cascading failures," Resources Policy, Elsevier, vol. 85(PB).
    10. Yujing Wang & Fu Ren & Ruoxin Zhu & Qingyun Du, 2020. "An Exploratory Analysis of Networked and Spatial Characteristics of International Natural Resource Trades (2000–2016)," Sustainability, MDPI, vol. 12(18), pages 1-34, September.
    11. Jungyeol Hong & Reuben Tamakloe & Soobeom Lee & Dongjoo Park, 2019. "Exploring the Topological Characteristics of Complex Public Transportation Networks: Focus on Variations in Both Single and Integrated Systems in the Seoul Metropolitan Area," Sustainability, MDPI, vol. 11(19), pages 1-26, September.
    12. Chen, Sai & Ding, Yueting & Zhang, Yanfang & Zhang, Ming & Nie, Rui, 2022. "Study on the robustness of China's oil import network," Energy, Elsevier, vol. 239(PB).
    13. Saxena, Chandni & Doja, M.N. & Ahmad, Tanvir, 2018. "Group based centrality for immunization of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 35-47.
    14. Ibnoulouafi, Ahmed & El Haziti, Mohamed, 2018. "Density centrality: identifying influential nodes based on area density formula," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 69-80.
    15. 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.
    16. Zhang, Dezhi & Zhang, Fangtao & Liang, Yijing, 2021. "An evolutionary model of the international logistics network based on the Belt and Road perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 572(C).
    17. Mitra, Satanik & Jenamani, Mamata, 2020. "OBIM: A computational model to estimate brand image from online consumer review," Journal of Business Research, Elsevier, vol. 114(C), pages 213-226.

    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. 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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Ma, Qian & Ma, Jun, 2017. "Identifying and ranking influential spreaders in complex networks with consideration of spreading probability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 312-330.
    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. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. Zhu, Hengmin & Yin, Xicheng & Ma, Jing & Hu, Wei, 2016. "Identifying the main paths of information diffusion in online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 320-328.
    16. Wu, Yanlei & Yang, Yang & Jiang, Fei & Jin, Shuyuan & Xu, Jin, 2014. "Coritivity-based influence maximization in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 467-480.
    17. Wang, Junyi & Hou, Xiaoni & Li, Kezan & Ding, Yong, 2017. "A novel weight neighborhood centrality algorithm for identifying influential spreaders in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 475(C), pages 88-105.
    18. 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.
    19. Ma, Tinghuai & Yue, Mingliang & Qu, Jingjing & Tian, Yuan & Al-Dhelaan, Abdullah & Al-Rodhaan, Mznah, 2018. "PSPLPA: Probability and similarity based parallel label propagation algorithm on spark," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 366-378.
    20. 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.

    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:452:y:2016:i:c:p:209-219. 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.