IDEAS home Printed from https://ideas.repec.org/a/wsi/ijmpcx/v28y2017i08ns0129183117501017.html
   My bibliography  Save this article

Link prediction via layer relevance of multiplex networks

Author

Listed:
  • Yabing Yao

    (School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, P. R. China)

  • Ruisheng Zhang

    (School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, P. R. China)

  • Fan Yang

    (School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, P. R. China)

  • Yongna Yuan

    (School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, P. R. China)

  • Qingshuang Sun

    (School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, P. R. China)

  • Yu Qiu

    (School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, P. R. China)

  • Rongjing Hu

    (School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, P. R. China)

Abstract

In complex networks, the existing link prediction methods primarily focus on the internal structural information derived from single-layer networks. However, the role of interlayer information is hardly recognized in multiplex networks, which provide more diverse structural features than single-layer networks. Actually, the structural properties and functions of one layer can affect that of other layers in multiplex networks. In this paper, the effect of interlayer structural properties on the link prediction performance is investigated in multiplex networks. By utilizing the intralayer and interlayer information, we propose a novel “Node Similarity Index” based on “Layer Relevance” (NSILR) of multiplex network for link prediction. The performance of NSILR index is validated on each layer of seven multiplex networks in real-world systems. Experimental results show that the NSILR index can significantly improve the prediction performance compared with the traditional methods, which only consider the intralayer information. Furthermore, the more relevant the layers are, the higher the performance is enhanced.

Suggested Citation

  • Yabing Yao & Ruisheng Zhang & Fan Yang & Yongna Yuan & Qingshuang Sun & Yu Qiu & Rongjing Hu, 2017. "Link prediction via layer relevance of multiplex networks," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 28(08), pages 1-24, August.
    • Handle: RePEc:wsi:ijmpcx:v:28:y:2017:i:08:n:s0129183117501017
    DOI: 10.1142/S0129183117501017
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0129183117501017
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S0129183117501017?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.

    Citations

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


    Cited by:

    1. Karimi, Fatemeh & Lotfi, Shahriar & Izadkhah, Habib, 2021. "Community-guided link prediction in multiplex networks," Journal of Informetrics, Elsevier, vol. 15(4).
    2. Nasiri, Elahe & Berahmand, Kamal & Li, Yuefeng, 2021. "A new link prediction in multiplex networks using topologically biased random walks," Chaos, Solitons & Fractals, Elsevier, vol. 151(C).
    3. Chunning Wang & Fengqin Tang & Xuejing Zhao, 2023. "LPGRI: A Global Relevance-Based Link Prediction Approach for Multiplex Networks," Mathematics, MDPI, vol. 11(14), pages 1-15, July.
    4. Tofighy, Sajjad & Charkari, Nasrollah Moghadam & Ghaderi, Foad, 2022. "Link prediction in multiplex networks using intralayer probabilistic distance and interlayer co-evolving factors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    5. Abdolhosseini-Qomi, Amir Mahdi & Yazdani, Naser & Asadpour, Masoud, 2020. "Overlapping communities and the prediction of missing links in multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(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:wsi:ijmpcx:v:28:y:2017:i:08:n:s0129183117501017. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/ijmpc/ijmpc.shtml .

    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.