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' ${\tau }_b$ is a considerably more robust measure of the degree-degree association.

References in corpus (3)

• Statistical physics of social dynamics
• Zero Pearson Coefficient for Strongly Correlated Growing Trees
• Generation of arbitrarily two-point correlated random networks

Cited by in corpus (9)

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• Degree-degree correlations in random graphs with heavy-tailed degrees
• Lower bound of assortativity coefficient in scale-free networks
• Fractality and degree correlations in scale-free networks
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• PageRank and rank-reversal dependence on the damping factor
• Assortativity measures for weighted and directed networks
• The scaling of human interactions with city size
• Large deviation and anomalous fluctuations scaling in degree assortativity on configuration networks