IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v512y2018icp165-173.html
   My bibliography  Save this article

Inter-layer similarity-based eigenvector centrality measures for temporal networks

Author

Listed:
  • Yin, Ran-Ran
  • Guo, Qiang
  • Yang, Jian-Nan
  • Liu, Jian-Guo

Abstract

Identifying the influential nodes in temporal networks has attracted lots of attention recently. In this paper, we present an Improved Eigenvector-based Centrality Measures (IECM) for temporal networks by regarding the coupling strength between proximity layers as the inter-layer similarity. Compared with the results of the nodes’ influences got by temporal global efficiency for two real networks, the IECM method could identify influential nodes more accurately than the traditional ECM method. Regarding to the fact that different kinds of measurements have different performances, we introduce the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method to measure the global performance. Specially, when the inter-layer coupling strength ω set as 1 in the ECM method, the accuracy could be averagely enhanced 18.75% and 29.65% at each time layer for Workspace and Enrons datasets respectively, which indicates that measuring the inter-layer coupling strength plays an important role for identifying the influential nodes.

Suggested Citation

  • Yin, Ran-Ran & Guo, Qiang & Yang, Jian-Nan & Liu, Jian-Guo, 2018. "Inter-layer similarity-based eigenvector centrality measures for temporal networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 165-173.
    • Handle: RePEc:eee:phsmap:v:512:y:2018:i:c:p:165-173
    DOI: 10.1016/j.physa.2018.08.018
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437118309634
    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.2018.08.018?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. Gã‰Nois, Mathieu & Vestergaard, Christian L. & Fournet, Julie & Panisson, Andr㉠& Bonmarin, Isabelle & Barrat, Alain, 2015. "Data on face-to-face contacts in an office building suggest a low-cost vaccination strategy based on community linkers," Network Science, Cambridge University Press, vol. 3(3), pages 326-347, September.
    2. Tao Zhou & Linyuan Lü & Yi-Cheng Zhang, 2009. "Predicting missing links via local information," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 71(4), pages 623-630, October.
    3. 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.
    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. Tao, Li & Kong, Shengzhou & He, Langzhou & Zhang, Fan & Li, Xianghua & Jia, Tao & Han, Zhen, 2022. "A sequential-path tree-based centrality for identifying influential spreaders in temporal networks," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    2. Jiang, Jiu-Lei & Fang, Hui & Li, Sheng-Qing & Li, Wei-Min, 2022. "Identifying important nodes for temporal networks based on the ASAM model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    3. Pradhan, Priodyuti & C.U., Angeliya & Jalan, Sarika, 2020. "Principal eigenvector localization and centrality in networks: Revisited," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(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. Chi, Kuo & Qu, Hui & Yin, Guisheng, 2022. "Link prediction for existing links in dynamic networks based on the attraction force," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    2. 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.
    3. Tao, Li & Kong, Shengzhou & He, Langzhou & Zhang, Fan & Li, Xianghua & Jia, Tao & Han, Zhen, 2022. "A sequential-path tree-based centrality for identifying influential spreaders in temporal networks," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    4. 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).
    5. 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.
    6. Karimi, Fatemeh & Lotfi, Shahriar & Izadkhah, Habib, 2021. "Community-guided link prediction in multiplex networks," Journal of Informetrics, Elsevier, vol. 15(4).
    7. Shang, Ronghua & Zhang, Weitong & Jiao, Licheng & Stolkin, Rustam & Xue, Yu, 2017. "A community integration strategy based on an improved modularity density increment for large-scale networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 471-485.
    8. Lin, Dan & Wu, Jiajing & Xuan, Qi & Tse, Chi K., 2022. "Ethereum transaction tracking: Inferring evolution of transaction networks via link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    9. Chen, Ling-Jiao & Zhang, Zi-Ke & Liu, Jin-Hu & Gao, Jian & Zhou, Tao, 2017. "A vertex similarity index for better personalized recommendation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 607-615.
    10. Luo, Mengzhuo & Liu, Xinzhi & Zhong, Shouming & Cheng, Jun, 2018. "Synchronization of multi-stochastic-link complex networks via aperiodically intermittent control with two different switched periods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 20-38.
    11. Xinyu Huang & Dongming Chen & Dongqi Wang & Tao Ren, 2020. "MINE: Identifying Top- k Vital Nodes in Complex Networks via Maximum Influential Neighbors Expansion," Mathematics, MDPI, vol. 8(9), pages 1-25, August.
    12. Andreas Spitz & Anna Gimmler & Thorsten Stoeck & Katharina Anna Zweig & Emőke-Ágnes Horvát, 2016. "Assessing Low-Intensity Relationships in Complex Networks," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-17, April.
    13. Ricardo S. Santos & Jose Soares & Pedro Carmona Marques & Helena V. G. Navas & José Moleiro Martins, 2021. "Integrating Business, Social, and Environmental Goals in Open Innovation through Partner Selection," Sustainability, MDPI, vol. 13(22), pages 1-25, November.
    14. Liu, Chuang & Zhou, Wei-Xing, 2012. "Heterogeneity in initial resource configurations improves a network-based hybrid recommendation algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5704-5711.
    15. Shenshen Bai & Longjie Li & Jianjun Cheng & Shijin Xu & Xiaoyun Chen, 2018. "Predicting Missing Links Based on a New Triangle Structure," Complexity, Hindawi, vol. 2018, pages 1-11, December.
    16. Xia, Yongxiang & Pang, Wenbo & Zhang, Xuejun, 2021. "Mining relationships between performance of link prediction algorithms and network structure," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    17. Qiaoran Yang & Zhiliang Dong & Yichi Zhang & Man Li & Ziyi Liang & Chao Ding, 2021. "Who Will Establish New Trade Relations? Looking for Potential Relationship in International Nickel Trade," Sustainability, MDPI, vol. 13(21), pages 1-15, October.
    18. Weihua Lei & Luiz G. A. Alves & Luís A. Nunes Amaral, 2022. "Forecasting the evolution of fast-changing transportation networks using machine learning," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    19. Li, Wei & Huang, Ce & Wang, Miao & Chen, Xi, 2017. "Stepping community detection algorithm based on label propagation and similarity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 472(C), pages 145-155.
    20. Rafiee, Samira & Salavati, Chiman & Abdollahpouri, Alireza, 2020. "CNDP: Link prediction based on common neighbors degree penalization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(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:phsmap:v:512:y:2018:i:c:p:165-173. 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.