k-core structure of real multiplex networks

Abstract

Multiplex networks are convenient mathematical representations for many real-world—biological, social, and technological—systems of interacting elements, where pairwise interactions among elements have different flavors. Previous studies pointed out that real-world multiplex networks display significant interlayer correlations—degree-degree correlation, edge overlap, node similarities—able to make them robust against random and targeted failures of their individual components. Here, we show that interlayer correlations are important also in the characterization of their k-core structure, namely, the organization in shells of nodes with an increasingly high degree. Understanding of k-core structures is important in the study of spreading processes taking place on networks, as for example in the identification of influential spreaders and the emergence of localization phenomena. We find that, if the degree distribution of the network is heterogeneous, then a strong k-core structure is well predicted by significantly positive degree-degree correlations. However, if the network degree distribution is homogeneous, then strong k-core structure is due to positive correlations at the level of node similarities. We reach our conclusions by analyzing different real-world multiplex networks, introducing novel techniques for controlling interlayer correlations of networks without changing their structure, and taking advantage of synthetic network models with tunable levels of interlayer correlations.