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

Abstract

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.