Exploring the landscape of dismantling strategies based on the community structure of networks.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
02 Sep 2023
02 Sep 2023
Historique:
received:
26
04
2023
accepted:
17
08
2023
medline:
3
9
2023
pubmed:
3
9
2023
entrez:
2
9
2023
Statut:
epublish
Résumé
Network dismantling is a relevant research area in network science, gathering attention both from a theoretical and an operational point of view. Here, we propose a general framework for dismantling that prioritizes the removal of nodes that bridge together different network communities. The strategies we detect are not unique, as they depend on the specific realization of the community detection algorithm considered. However, when applying the methodology to some synthetic benchmark and real-world networks we find that the dismantling performances are strongly robust, and do not depend on the specific algorithm. Thus, the stochasticity inherently present in many community detection algorithms allows to identify several strategies that have comparable effectiveness but require the removal of distinct subsets of nodes. This feature is highly relevant in operational contexts in which the removal of nodes is costly and allows to identify the least expensive strategy that still holds high effectiveness.
Identifiants
pubmed: 37660149
doi: 10.1038/s41598-023-40867-2
pii: 10.1038/s41598-023-40867-2
pmc: PMC10475058
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
14448Informations de copyright
© 2023. Springer Nature Limited.
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