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.

arXiv admin note: substantial text overlap with arXiv:2005.04432

References in corpus (17)

• The structure of scientific collaboration networks
• Multilayer Networks
• Link Prediction in Complex Networks: A Survey
• Hierarchical structure and the prediction of missing links in networks
• Missing and spurious interactions and the reconstruction of complex networks
• Structural measures for multiplex networks
• Layer aggregation and reducibility of multilayer interconnected networks
• Link Prediction Based on Local Random Walk
• Emergence of network features from multiplexity
• Diffusion of scientific credits and the ranking of scientists
• An Evolutionary Algorithm Approach to Link Prediction in Dynamic Social Networks
• Weighted Multiplex Networks
• Multilayer Networks in a Nutshell
• Hidden geometric correlations in real multiplex networks
• Structure of Triadic Relations in Multiplex Networks
• Link Prediction in Multiplex Networks based on Interlayer Similarity
• Effects of temporal correlations in social multiplex networks

Cited by in corpus (3)

• Multilayer Network Science: from Cells to Societies
• Information Evolution in Complex Networks
• Reconstructing Sparse Multiplex Networks with Application to Covert Networks