Link prediction via layer relevance of multiplex networks

Yabing Yao (), Ruisheng Zhang, Fan Yang (), Yongna Yuan (), Qingshuang Sun (), Yu Qiu () and Rongjing Hu ()
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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

International Journal of Modern Physics C (IJMPC), 2017, vol. 28, issue 08, 1-24

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.

Keywords: Complex networks; layer relevance; link prediction; multiplex networks; node similarity (search for similar items in EconPapers)
Date: 2017
References: View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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DOI: 10.1142/S0129183117501017

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