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- W2990865504 abstract "Since the seminal work of Litvak and van der Hofstad [12], it has been known that Newman’s assortativity [14, 15], being based on Pearson’s correlation, is subject to a pernicious size effect which makes large networks with heavy-tailed degree distributions always unassortative. Usage of Spearman’s (rho ), or even Kendall’s (tau ) was suggested as a replacement [6], but the treatment of ties was problematic for both measures. In this paper we first argue analytically that the tie-aware version of (tau ) solves the problems observed in [6], and we show that Newman’s assortativity is heavily influenced by tightly knit communities. Then, we perform for the first time a set of large-scale computational experiments on a variety of networks, comparing assortativity based on Kendall’s (tau ) and assortativity based on Pearson’s correlation, showing that the pernicious effect of size is indeed very strong on real-world large networks, whereas the tie-aware Kendall’s (tau ) can be a practical, principled alternative." @default.
- W2990865504 created "2019-12-05" @default.
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- W2990865504 date "2019-11-25" @default.
- W2990865504 modified "2023-09-23" @default.
- W2990865504 title "The Case for Kendall’s Assortativity" @default.
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- W2990865504 doi "https://doi.org/10.1007/978-3-030-36683-4_24" @default.
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