IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v39y2024i2d10.1007_s00180-023-01334-8.html
   My bibliography  Save this article

A comprehensive framework for link prediction in multiplex networks

Author

Listed:
  • Fengqin Tang

    (Huaibei Normal University)

  • Cuixia Li

    (Xuzhou University of Technology)

  • Chungning Wang

    (Lanzhou University of Finance and Economics)

  • Yi Yang

    (Huaibei Normal University)

  • Xuejing Zhao

    (Lanzhou University)

Abstract

The idea of predicting links in multiplex networks has gained increasing interest in recent years. In this paper, we propose a comprehensive framework which benefits from the structural information of auxiliary layers to predict links on a target layer of multiplex networks. Specifically, we assume that the likelihood of the existence of a link between two nodes is determined by the contributions from both the nodes’ neighbors on the target layer and their counterparts’ neighbors on a manually network generated by auxiliary layers. The final likelihood matrix is acquired by an iterative algorithm. In addition, we show advantages of our methods for predicting links on sparse and dense networks as well as on networks with assortative and disassortative structural layers. The effectiveness of the proposed methods are evaluated through extensive experiments on real-world multiplex networks.

Suggested Citation

  • Fengqin Tang & Cuixia Li & Chungning Wang & Yi Yang & Xuejing Zhao, 2024. "A comprehensive framework for link prediction in multiplex networks," Computational Statistics, Springer, vol. 39(2), pages 939-961, April.
    • Handle: RePEc:spr:compst:v:39:y:2024:i:2:d:10.1007_s00180-023-01334-8
    DOI: 10.1007/s00180-023-01334-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00180-023-01334-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00180-023-01334-8?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. Yasami, Yasser & Safaei, Farshad, 2018. "A novel multilayer model for missing link prediction and future link forecasting in dynamic complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 2166-2197.
    2. L. Bargigli & G. di Iasio & L. Infante & F. Lillo & F. Pierobon, 2015. "The multiplex structure of interbank networks," Quantitative Finance, Taylor & Francis Journals, vol. 15(4), pages 673-691, April.
    3. Lü, Linyuan & Zhou, Tao, 2011. "Link prediction in complex networks: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(6), pages 1150-1170.
    4. Leo Katz, 1953. "A new status index derived from sociometric analysis," Psychometrika, Springer;The Psychometric Society, vol. 18(1), pages 39-43, March.
    5. Manlio De Domenico & Vincenzo Nicosia & Alexandre Arenas & Vito Latora, 2015. "Structural reducibility of multilayer networks," Nature Communications, Nature, vol. 6(1), pages 1-9, November.
    Full references (including those not matched with items on IDEAS)

    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. Zhang, Pei-wen & Zhao, Lian-zheng & Wang, Yu & Ding, Rui & Du, Fu-min, 2024. "Air route link prediction based on the PSO-CLP model," Journal of Air Transport Management, Elsevier, vol. 120(C).
    2. Karimi, Fatemeh & Lotfi, Shahriar & Izadkhah, Habib, 2021. "Community-guided link prediction in multiplex networks," Journal of Informetrics, Elsevier, vol. 15(4).
    3. Najari, Shaghayegh & Salehi, Mostafa & Ranjbar, Vahid & Jalili, Mahdi, 2019. "Link prediction in multiplex networks based on interlayer similarity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    4. Fetta, Angelico & Harper, Paul & Knight, Vincent & Williams, Janet, 2018. "Predicting adolescent social networks to stop smoking in secondary schools," European Journal of Operational Research, Elsevier, vol. 265(1), pages 263-276.
    5. 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.
    6. Liu, Shuxin & Ji, Xinsheng & Liu, Caixia & Bai, Yi, 2017. "Extended resource allocation index for link prediction of complex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 174-183.
    7. Sherkat, Ehsan & Rahgozar, Maseud & Asadpour, Masoud, 2015. "Structural link prediction based on ant colony approach in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 80-94.
    8. Elosegui, Pedro & Forte, Federico D. & Montes-Rojas, Gabriel, 2022. "Network structure and fragmentation of the Argentinean interbank markets," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 3(3).
    9. Lee, Jiho & Ko, Namuk & Yoon, Janghyeok & Son, Changho, 2021. "An approach for discovering firm-specific technology opportunities: Application of link prediction to F-term networks," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
    10. Chungmok Lee & Minh Pham & Myong K. Jeong & Dohyun Kim & Dennis K. J. Lin & Wanpracha Art Chavalitwongse, 2015. "A Network Structural Approach to the Link Prediction Problem," INFORMS Journal on Computing, INFORMS, vol. 27(2), pages 249-267, May.
    11. Wang, Zuxi & Wu, Yao & Li, Qingguang & Jin, Fengdong & Xiong, Wei, 2016. "Link prediction based on hyperbolic mapping with community structure for complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 609-623.
    12. Kim, Hyunwoo & Hong, Suckwon & Kwon, Ohjin & Lee, Changyong, 2017. "Concentric diversification based on technological capabilities: Link analysis of products and technologies," Technological Forecasting and Social Change, Elsevier, vol. 118(C), pages 246-257.
    13. Temizsoy, Asena & Iori, Giulia & Montes-Rojas, Gabriel, 2017. "Network centrality and funding rates in the e-MID interbank market," Journal of Financial Stability, Elsevier, vol. 33(C), pages 346-365.
    14. Xie He & Amir Ghasemian & Eun Lee & Alice C Schwarze & Aaron Clauset & Peter J Mucha, 2024. "Link prediction accuracy on real-world networks under non-uniform missing-edge patterns," PLOS ONE, Public Library of Science, vol. 19(7), pages 1-17, July.
    15. Lee, Yan-Li & Zhou, Tao, 2021. "Collaborative filtering approach to link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 578(C).
    16. Moradabadi, Behnaz & Meybodi, Mohammad Reza, 2016. "Link prediction based on temporal similarity metrics using continuous action set learning automata," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 460(C), pages 361-373.
    17. Wu, Jianshe & Hou, Yunting & Jiao, Yang & Li, Yong & Li, Xiaoxiao & Jiao, Licheng, 2015. "Density shrinking algorithm for community detection with path based similarity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 433(C), pages 218-228.
    18. Ma, Xiaoke & Sun, Penggang & Wang, Yu, 2018. "Graph regularized nonnegative matrix factorization for temporal link prediction in dynamic networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 121-136.
    19. 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.
    20. Mungo, Luca & Lafond, François & Astudillo-Estévez, Pablo & Farmer, J. Doyne, 2023. "Reconstructing production networks using machine learning," Journal of Economic Dynamics and Control, Elsevier, vol. 148(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:spr:compst:v:39:y:2024:i:2:d:10.1007_s00180-023-01334-8. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.