Link prediction in multiplex networks via triadic closure

Abstract

Link prediction algorithms can help to understand the structure and dynamics of complex systems, to reconstruct networks from incomplete data sets, and to forecast future interactions in evolving networks. Available algorithms based on similarity between nodes are bounded by the limited amount of links present in these networks. In this Rapid Communication, we reduce this latter intrinsic limitation and show that different kinds of relational data can be exploited to improve the prediction of new links. To this aim, we propose a link prediction algorithm by generalizing the Adamic-Adar method to multiplex networks composed by an arbitrary number of layers, that encode diverse forms of interactions. We show that this metric outperforms the classical single-layered Adamic-Adar score and other state-of-the-art methods, across several social, biological, and technological systems. As a by-product, the coefficients that maximize the multiplex Adamic-Adar metric indicate how the information structured in a multiplex network can be optimized for the link prediction task, revealing which layers are redundant. Interestingly, this effect can be asymmetric with respect to predictions in different layers. Our work paves the way for a deeper understanding of the role of different relational data in predicting new interactions and provides another algorithm for link prediction in multiplex networks that can be applied to a plethora of systems.