Abstract
Recently multilayer networks are introduced to model real systems. In these models the individuals make connection in multiple layers. Transportation networks, biological systems and social networks are some examples of multilayer networks. There are various link prediction algorithms for single-layer networks and some of them have been recently extended to multilayer networks. In this manuscript, we propose a new link prediction algorithm for multiplex networks using two novel similarity metrics based on the hyperbolic distance of node pairs. We use the proposed methods to predict spurious and missing links in multiplex networks. Missing links are those links that may appear in the future evolution of the network, while spurious links are the existing connections that are unlikely to appear if the network is evolving normally. One may interpret spurious links as abnormal links in the network. We apply the proposed algorithm on real-world multiplex networks and the numerical simulations reveal its superiority than the state-of-the-art algorithms.
Highlights
Many real biological, social and technological systems are modeled as networks in which nodes and links represent entities and different kinds of connections respectively
We investigate the node similarity index based on hyperbolic geometry and the layer relevance of the multiplex networks for predicting the spurious and missing links
We perform experiments on four single-layer synthetic networks to evaluate the similarity measures and six real multilayer networks to investigate the impact of interlayer information
Summary
Social and technological systems are modeled as networks in which nodes and links represent entities and different kinds of connections respectively. The study of hyperbolic geometry based on the network structure has become useful in solving the link prediction problem. We investigate the node similarity index based on hyperbolic geometry and the layer relevance of the multiplex networks for predicting the spurious and missing links. Using the interlayer information in solving the missing link prediction in multiplex networks has been considered before in a number of works. Sharma et al.[16] proposed a new method considering weight for each layer of multiplex network and used it to solve the link prediction problem in the target layer. Www.nature.com/scientificreports based on interlayer and intralayer information to solve the missing link prediction problem in multiplex networks. They proposed a method to employ interlayer information to improve the performance of spurious link prediction in the target layer
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
