TY - JOUR
T1 - Interplay between k -core and community structure in complex networks
AU - Malvestio, Irene
AU - Cardillo, Alessio
AU - Masuda, Naoki
N1 - Funding Information:
The authors thank A. Barrat for helpful comments on a preliminary version of this work. A.C. acknowledges the support of the Spanish Ministerio de Ciencia e Innovacion (MICINN) through Grant IJCI-2017-34300. I.M., A.C., and N.M. acknowledge the support of Cookpad Limited. This work was carried out using the computational facilities of the Advanced Computing Research Centre, University of Bristol – http://www.bristol.ac.uk/acrc/. Numerical analysis has been carried out using the NumPy, NetworkX, and Graph-tool Python packages69–72. Graphics have been prepared using the Matplotlib Python package73.
Publisher Copyright:
© 2020, The Author(s).
PY - 2020/12/1
Y1 - 2020/12/1
N2 - The organisation of a network in a maximal set of nodes having at least k neighbours within the set, known as k-core decomposition, has been used for studying various phenomena. It has been shown that nodes in the innermost k-shells play a crucial role in contagion processes, emergence of consensus, and resilience of the system. It is known that the k-core decomposition of many empirical networks cannot be explained by the degree of each node alone,or equivalently, random graph models that preserve the degree of each node (i.e., configuration model). Here we study the k-core decomposition of some empirical networks as well as that of some randomised counterparts, and examine the extent to which the k-shell structure of the networks can be accounted for by the community structure.We find that preserving the community structure in the randomisation process is crucial for generating networks whose k-core decomposition is close to the empirical one. We also highlight the existence, in some networks, of a concentration of the nodes in the innermost k-shells into a small number of communities.
AB - The organisation of a network in a maximal set of nodes having at least k neighbours within the set, known as k-core decomposition, has been used for studying various phenomena. It has been shown that nodes in the innermost k-shells play a crucial role in contagion processes, emergence of consensus, and resilience of the system. It is known that the k-core decomposition of many empirical networks cannot be explained by the degree of each node alone,or equivalently, random graph models that preserve the degree of each node (i.e., configuration model). Here we study the k-core decomposition of some empirical networks as well as that of some randomised counterparts, and examine the extent to which the k-shell structure of the networks can be accounted for by the community structure.We find that preserving the community structure in the randomisation process is crucial for generating networks whose k-core decomposition is close to the empirical one. We also highlight the existence, in some networks, of a concentration of the nodes in the innermost k-shells into a small number of communities.
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U2 - 10.1038/s41598-020-71426-8
DO - 10.1038/s41598-020-71426-8
M3 - Article
C2 - 32895432
AN - SCOPUS:85090352130
SN - 2045-2322
VL - 10
JO - Scientific reports
JF - Scientific reports
IS - 1
M1 - 14702
ER -