IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v420y2015icp277-283.html
   My bibliography  Save this article

Weighted k-shell decomposition for complex networks based on potential edge weights

Author

Listed:
  • Wei, Bo
  • Liu, Jie
  • Wei, Daijun
  • Gao, Cai
  • Deng, Yong

Abstract

Identifying influential nodes in complex networks has attracted much attention because of its great theoretical significance and wide application. Existing methods consider the edges equally when designing identifying methods for the unweighted networks. In this paper, we propose an edge weighting method based on adding the degree of its two end nodes and for the constructed weighted networks, we propose a weighted k-shell decomposition method (Wks). Further investigations on the epidemic spreading process of the Susceptible–Infected–Recovered (SIR) model and Susceptible–Infected (SI) model in real complex networks verify that our method is effective for detecting the node influence.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:420:y:2015:i:c:p:277-283
    DOI: 10.1016/j.physa.2014.11.012
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437114009637
    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.2014.11.012?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. Zhu, Zhiguo, 2013. "Discovering the influential users oriented to viral marketing based on online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(16), pages 3459-3469.
    3. 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.
    4. 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.
    5. 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.
    6. Hao Liao & An Zeng & Rui Xiao & Zhuo-Ming Ren & Duan-Bing Chen & Yi-Cheng Zhang, 2014. "Ranking Reputation and Quality in Online Rating Systems," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-7, May.
    7. Yang-Yu Liu & Jean-Jacques Slotine & Albert-László Barabási, 2011. "Controllability of complex networks," Nature, Nature, vol. 473(7346), pages 167-173, May.
    8. 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.
    9. 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.
    10. Zhang, Cheng-Jun & Zeng, An, 2014. "Network skeleton for synchronization: Identifying redundant connections," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 402(C), pages 180-185.
    11. 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.
    12. He, Zhiwei & Liu, Shuai & Zhan, Meng, 2013. "Dynamical robustness analysis of weighted complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(18), pages 4181-4191.
    13. 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.
    14. 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.
    15. 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.
    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. 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.
    2. Fink, Christian G. & Fullin, Kelly & Gutierrez, Guillermo & Omodt, Nathan & Zinnecker, Sydney & Sprint, Gina & McCulloch, Sean, 2023. "A centrality measure for quantifying spread on weighted, directed networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    3. Eidsaa, Marius & Almaas, Eivind, 2016. "Investigating the relationship between k-core and s-core network decompositions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 449(C), pages 111-125.
    4. Al-Azim, Nouran Ayman R. Abd & Gharib, Tarek F. & Afify, Yasmine & Hamdy, Mohamed, 2020. "Influence propagation: Interest groups and node ranking models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    5. 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).
    6. Al-garadi, Mohammed Ali & Varathan, Kasturi Dewi & Ravana, Sri Devi, 2017. "Identification of influential spreaders in online social networks using interaction weighted K-core decomposition method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 278-288.
    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.

    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. 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.
    2. 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.
    3. Liu, Yang & Wei, Bo & Du, Yuxian & Xiao, Fuyuan & Deng, Yong, 2016. "Identifying influential spreaders by weight degree centrality in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 86(C), pages 1-7.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Ma, Ling-ling & Ma, Chuang & Zhang, Hai-Feng & Wang, Bing-Hong, 2016. "Identifying influential spreaders in complex networks based on gravity formula," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 205-212.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. Ni, Chengzhang & Yang, Jun & Kong, Demei, 2020. "Sequential seeding strategy for social influence diffusion with improved entropy-based centrality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    20. 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.

    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:420:y:2015:i:c:p:277-283. 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.