Since the seminal work of Litvak and van der Hofstad , 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 , or even Kendall’s was suggested as a replacement , but the treatment of ties was problematic for both measures. In this paper we first argue analytically that the tie-aware version of solves the problems observed in , 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 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 can be a practical, principled alternative.
The Case for Kendall’s Assortativity / P. Boldi, S. Vigna (STUDIES IN COMPUTATIONAL INTELLIGENCE). - In: Complex Networks and Their Applications VIII. 2 / [a cura di] H. Cherifi, S. Gaito, J.F. Mendes, E. Moro, L.M. Rocha. - [s.l] : Springer, 2020. - ISBN 9783030366827. - pp. 291-302 (( convegno Eighth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS tenutosi a Lisbon nel 2019.
|Titolo:||The Case for Kendall’s Assortativity|
|Settore Scientifico Disciplinare:||Settore INF/01 - Informatica|
|Data di pubblicazione:||2020|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1007/978-3-030-36683-4_24|
|Tipologia:||Book Part (author)|
|Appare nelle tipologie:||03 - Contributo in volume|