title: Measuring degree-degree association in networks.
abstract: The Pearson correlation coefficient is commonly used for quantifying the global level of degree-degree association in complex networks. Here, we use a probabilistic representation of the underlying network structure for assessing the applicability of different association measures to heavy-tailed degree distributions. Theoretical arguments together with our numerical study indicate that Pearson's coefficient often depends on the size of networks with equal association structure, impeding a systematic comparison of real-world networks. In contrast, Kendall-Gibbons' τ{b} is a considerably more robust measure of the degree-degree association.
authors.affiliation: Laboratory for Safety Analysis, ETH Zurich, CH-8092 Zurich, Switzerland.
authors.forename: Mathias
authors.initials: M
authors.lastname: Raschke
authors.forename: Markus
authors.initials: M
authors.lastname: Schläpfer
authors.forename: Roberto
authors.initials: R
authors.lastname: Nibali
mesh_terms: D008962:Models, Theoretical; D011336:Probability; D013269:Stochastic Processes