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 work, we reduce this latter intrinsic limitation and show that different kind of relational data can be exploited to improve the prediction of new links. To this aim, we propose a novel 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 the new 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 byproduct, 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 a new algorithm for link prediction in multiplex networks that can be applied to a plethora of systems.

Prediction of new scientific collaborations through multiplex networks

“…The quality of link prediction algorithms is usually evaluated by the Receiver Operating Characteristics (ROC) curve, with the corresponding Area Under the Curve (AUC) value. However, due to the limited amount of links present in a network, the AUC of any similarity-based link prediction algorithm is bounded [31,35]. For this reason, we also consider the Precision of different scores, computed as n * /n, where n is the number of new links that we want to predict and n * is the amount of correct predictions among the top n links.…”

“…Our findings show that metrics based on triadic closure generally outperform simpler dyadic scores, and that the contributions of different layers are bounded by the amount of information available in each layer. For this reason, the best results, both in terms of Precision and AUC, are obtained by combining the information present in different layers by means of the Multiplex Adamic-Adar score [31], that fully exploits the multiplex nature of the scientific networks reconstructed here. The coefficients that maximize the Multiplex Adamic-Adar metric indicate how the information structured in the multiplex network can be optimized for the prediction of new scientific collaborations.…”

“…Finally, we consider a recently proposed generalization of the AA score [19] to multiplex networks, which takes into account all possible triadic closures in multiplex networks [31]. The MAA score for the prediction of a link between nodes u and v in the collaboration layer c is defined as…”

“…where T αβ are different kinds of triadic relations among three nodes u, v and w [31]. While the link uv to be predicted is in the collaboration layer, the other two links uw and vw may lay in any layer.…”

“…We compare several link prediction algorithms based on different dyadic and triadic interactions between scientists. We also consider a recently proposed metric for link prediction in multiplex networks, based on a generalization of the Adamic-Adar method for single-layered networks [31], able to fully exploit the multiplex representation of scientific networks. We show that scientific credit and common scientific interests can be predictive of new collaborations between scientists.…”