Disrupting resilient criminal networks through data analysis: The case of Sicilian Mafia
- Submitting institution
-
University of Derby
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 782275-1
- Type
- D - Journal article
- DOI
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10.1371/journal.pone.0236476
- Title of journal
- PLOS ONE
- Article number
- -
- First page
- e0236476
- Volume
- 15
- Issue
- 8
- ISSN
- 1932-6203
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2020
- URL
-
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0236476
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
7
- Research group(s)
-
-
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper uses Social Network Analysis methods to unveil the structure and to disrupt resilient criminal networks. Its significance is threefold: 1. The real datasets from juridical acts presenting calls and meetings between Sicilian Mafia members (https://github.com/lcucav/networkdisruption) received interest and analysis (http://www.cs.rpi.edu/~szymansk, https://maelfabien.github.io/machinelearning/sicilian/#data). 2. Our algorithms simulating intervention procedures (sequential arrest and police raids) revealed that betweenness centrality is the best strategy for prioritizing nodes to be removed. 3. Practical applications for Law Enforcing Agencies. The paper sparked significant interest from scientific newspapers (Eurekalert.org: https://bit.ly/3pNGpo0, Galileonet.it: https://bit.ly/3nBIcue, Ansa.it: https://bit.ly/3kMyp2F, zmescience.com: https://bit.ly/390CM81) and international magazines, receiving 3000 views in 3 months.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
