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arXiv · 1003.1634

Measuring degree-degree association in networks

Mathias Raschke·Markus Schläpfer·Roberto Nibali
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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.

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Mathias Raschke, Markus Schläpfer, Roberto Nibali. 2010-03-08. Measuring degree-degree association in networks. https://doi.org/10.1103/physreve.82.037102

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