Link prediction for multilayer networks using interlayer structural information

Fengqin Tang, Chunning Wang, Yuanyuan Wang and Jinxia Su
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Fengqin Tang: School of Mathematics Sciences, Huaibei Normal University, Huaibei, Anhui 235000, P. R. China
Chunning Wang: School of Mathematics and Statistics, Lanzhou University, Lanzhou, Gansu 73000, P. R. China3School of Statistics, Lanzhou University of Finance and Economics, Lanzhou Gansu 73000, P. R. China
Yuanyuan Wang: School of Mathematics and Statistics, Lanzhou University, Lanzhou, Gansu 73000, P. R. China3School of Statistics, Lanzhou University of Finance and Economics, Lanzhou Gansu 73000, P. R. China
Jinxia Su: School of Mathematics and Statistics, Lanzhou University, Lanzhou, Gansu 73000, P. R. China3School of Statistics, Lanzhou University of Finance and Economics, Lanzhou Gansu 73000, P. R. China

International Journal of Modern Physics C (IJMPC), 2022, vol. 33, issue 01, 1-22

Abstract: A multilayer network is a useful representation for real-world complex systems in which multiple types of connections are formed between entities. Connections of the same type form a specific layer of the network. We propose a novel framework for predicting links in a target layer of a multilayer network by taking into account the interlayer structural information. The method depends on the intuitive assumption that two node pairs in the target layer tend to have similar connection patterns if these pairs of nodes are similar. Further, the prediction accuracy will be improved in the target layer if the structural information of the copies of the node pairs in relevant layers is employed. We demonstrate the effectiveness of the proposed method experimentally by applying it to both simulated and real-world multilayer networks.

Keywords: Link prediction; multilayer network; semi-supervised learning; interlayer similarity (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0129183122500036

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