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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
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    Citations

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    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. 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).
    3. 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).
    4. 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.
    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).

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