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

Author:

Wang Chunning1,Tang Fengqin2,Zhao Xuejing1ORCID

Affiliation:

1. School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China

2. School of Mathematics Sciences, Huaibei Normal University, Huaibei 235000, China

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.

Funder

The National Natural Science Foundation of China

Natural Science Foundation of Anhui Province

Publisher

MDPI AG

Subject

General Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)

Reference50 articles.

1. Emergence of network features from multiplexity;Cardillo;Sci. Rep.,2013

2. Growing multiplex networks;Nicosia;Phys. Rev. Lett.,2013

3. Multirelational organization of large-scale social networks in an online world;Szell;Proc. Natl. Acad. Sci. USA,2010

4. Multilayer networks;Arenas;J. Complex Netw.,2014

5. Link prediction for multilayer networks using interlayer structural information;Tang;Int. J. Mod. Phys. C,2022

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