Network disruption via continuous batch removal: The case of Sicilian Mafia.

Autor: Jia M; School of Computer Science, University of Technology Sydney, Sydney, NSW, Australia., De Meo P; Department of Ancient and Modern Civilizations, University of Messina, Messina, Italy., Gabrys B; School of Computer Science, University of Technology Sydney, Sydney, NSW, Australia., Musial K; School of Computer Science, University of Technology Sydney, Sydney, NSW, Australia.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2024 Aug 21; Vol. 19 (8), pp. e0308722. Date of Electronic Publication: 2024 Aug 21 (Print Publication: 2024).
DOI: 10.1371/journal.pone.0308722
Abstrakt: Network disruption is pivotal in understanding the robustness and vulnerability of complex networks, which is instrumental in devising strategies for infrastructure protection, epidemic control, cybersecurity, and combating crime. In this paper, with a particular focus on disrupting criminal networks, we proposed to impose a within-the-largest-connected-component constraint in a continuous batch removal disruption process. Through a series of experiments on a recently released Sicilian Mafia network, we revealed that the constraint would enhance degree-based methods while weakening betweenness-based approaches. Moreover, based on the findings from the experiments using various disruption strategies, we propose a structurally-filtered greedy disruption strategy that integrates the effectiveness of greedy-like methods with the efficiency of structural-metric-based approaches. The proposed strategy significantly outperforms the longstanding state-of-the-art method of betweenness centrality while maintaining the same time complexity.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Jia et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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