Association of the PageRank algorithm with similarity-based methods for link prediction in complex networks.

Link prediction is an axial field in complex network analysis as it aims to infer new connections between nodes in a given network. Many applications of this task include: the reference system, the suggestion of friends in the social network and the prediction of interactions between proteins in biological networks. Several methods have been developed in the link prediction task, especially the similarity-based methods which are widely used due to their low complexity and good performance. In this paper, we propose a novel link prediction approach that combines the PageRank algorithm with local information-based methods to improve performance while retaining the advantage of low complexity of local methods. We conducted a series of experimental studies on eleven data sets where we compared our new combined methods with six well-known local methods. The results obtained show a significant gain in terms of performance in almost all data sets. In addition to this and to confirm the superiority of the proposed methods, another comparative study is performed, formed of nine local and global methods. According to the experimental results, our approach outperforms all other compared methods with linear complexity. • The analysis of networks evolution brings us to the problem of link prediction. • Similarity-based methods are used as a local method to predict new links. • New similarity measures between unconnected nodes that do not have shared neighbors. • Our method offers higher precision than some well-known local and global methods. • Our method offers linear computational complexity. [ABSTRACT FROM AUTHOR]