LPGRI: A Global Relevance-Based Link Prediction Approach for Multiplex Networks

Chunning Wang (), Fengqin Tang and Xuejing Zhao
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Chunning Wang: School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China
Fengqin Tang: School of Mathematics Sciences, Huaibei Normal University, Huaibei 235000, China
Xuejing Zhao: School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China

Mathematics, 2023, vol. 11, issue 14, 1-15

Abstract: The individuals of real-world networks participate in various types of connections, each forming a layer in multiplex networks. Link prediction is an important problem in multiplex network analysis owing to its wide range of practical applications, such as mining drug targets, recommending friends in social networks, and exploring network evolution mechanisms. A key issue of link prediction within multiplex networks is how to estimate the likelihood of potential links in the predicted layer by leveraging both interlayer and intralayer information. Several studies have shown that incorporating interlayer topological information can improve the performance of link prediction in the predicted layer. Therefore, this paper proposes the Link Prediction based on Global Relevance of Interlayer (LPGRI) method to estimate the likelihood of potential links in the predicted layer of multiplex networks, which comprehensively utilizes both types of information. In the LPGRI method, the contribution of interlayer information is determined using the global relevance (GR) index between layers. Experimental studies on six real multiplex networks demonstrate the competitive performance of our method.

Keywords: complex network; link prediction; multiplex network; interlay relevance (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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