Raschke M Schläpfer M Nibali R 2010 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. application/pdf https://sonar.ch/global/documents/154740 eng info:eu-repo/semantics/altIdentifier/doi/10.1103/PhysRevE.82.037102 info:eu-repo/semantics/altIdentifier/pmid/21230212 info:eu-repo/semantics/openAccess Physical review. E, Statistical, nonlinear, and soft matter physics. - 2010 Models, Theoretical Probability Stochastic Processes Measuring degree-degree association in networks. http://purl.org/coar/resource_type/c_6501