Link prediction in multiplex networks using intralayer probabilistic distance and interlayer co-evolving factors

Multiplex networks are very flexible at showing heterogeneous relationships between identical entities. Link prediction is a fundamental problem in network science. There are many studies on link prediction in complex networks, but few studies were conducted on link prediction in multiplex networks. This study proposes a method for estimating link likelihood in multiplex networks based on the Node-Accessibility-Distribution (NAD) and the co-evolving factors of layers. The NAD is introduced as a probabilistic measure to find local and pseudo-global structural features of nodes in layers of the multiplex network. The probabilistic distance among nodes is calculated using Jensen-Shannon diversity. Since the evolution of one layer subsequently affects the dynamics of other layers, this study introduces the co-evolving factors as criteria for determining the effect of the evolution of layers in the formation of new links in the target layer. In order to estimate the co-evolving factors, logistics regression and Maximum Likelihood Estimation(MLE) are employed. The proposed method is evaluated with six real-world datasets. The results show that the proposed approach has a better average AUC and precision than the state-of-the-art methods. Based on various datasets, the AUC and precision were improved by 1% to 5% compared with the state-of-the-art. (C) 2022 Elsevier B.V. All rights reserved.