ArticleScientific reports2014
The relative ineffectiveness of criminal network disruption.
Article in Scientific reports, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Explainable multi-agent learning for adaptive terrorist network disruption.Scientific reports · 2026Article
- Assessing the value of objective suspicious transactions in Dutch anti-money laundering investigations: a network approach.EPJ data science · 2026Article
- The paradox of intervention: Resilience in adaptive multirole coordination networks.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- The Synergistic Effects of Structural Evolution and Attack Strategies on Network Matching Robustness.Entropy (Basel, Switzerland) · 2025Article
- Structural roles and gender disparities in corruption networks.Scientific reports · 2025Article
- Network Dismantling on Signed Network by Evolutionary Deep Reinforcement Learning.Sensors (Basel, Switzerland) · 2024Article
- Criminal organizations exhibit hysteresis, resilience, and robustness by balancing security and efficiency.Scientific reports · 2024Article
- Article
- Using network analysis to identify leverage points based on causal loop diagrams leads to false inference.Scientific reports · 2023Article
- A large-scale empirical investigation of specialization in criminal career.Scientific reports · 2023Article
- Disrupting hate: The effect of deplatforming hate organizations on their online audience.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Reconstructing Sparse Multiplex Networks with Application to Covert Networks.Entropy (Basel, Switzerland) · 2023Article
- The Greentown Project: Building Evidence to inform Intervention Design for Juveniles Caught-up in Local Criminal Networks.International journal of offender therapy and comparative criminology · 2023Article
- Modeling the role of police corruption in the reduction of organized crime: Mexico as a case study.Scientific reports · 2022Article
- Machine learning partners in criminal networks.Scientific reports · 2022Article
- Universality of political corruption networks.Scientific reports · 2022Article
- Article
- Assessing police topological efficiency in a major sting operation on the dark web.Scientific reports · 2020Article
- The responsiveness of criminal networks to intentional attacks: Disrupting darknet drug trade.PloS one · 2020Article
- Disrupting resilient criminal networks through data analysis: The case of Sicilian Mafia.PloS one · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Researchers, policymakers and law enforcement agencies across the globe struggle to find effective strategies to control criminal networks. The effectiveness of disruption strategies is known to depend on both network topology and network resilience. However, as these criminal networks operate in secrecy, data-driven knowledge concerning the effectiveness of different criminal network disruption strategies is very limited. By combining computational modeling and social network analysis with unique criminal network intelligence data from the Dutch Police, we discovered, in contrast to common belief, that criminal networks might even become 'stronger', after targeted attacks. On the other hand increased efficiency within criminal networks decreases its internal security, thus offering opportunities for law enforcement agencies to target these networks more deliberately. Our results emphasize the importance of criminal network interventions at an early stage, before the network gets a chance to (re-)organize to maximum resilience. In the end disruption strategies force criminal networks to become more exposed, which causes successful network disruption to become a long-term effort.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.