Structural resilience and recovery of a criminal network after disruption: a simulation study

• Published in: Journal of experimental criminology Vol. 20; no. 3; pp. 883 - 911
• Main Author: Diviák, Tomáš
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.09.2024; Springer Nature B.V
• Subjects: Criminal investigations; Criminology; Criminology and Criminal Justice; Law and Criminolgy; Observational studies; Political Science; Simulation; Social network analysis; Social networks; Social Sciences; Simulations; Interventions; Network mechanisms; Stochastic actor-oriented models; Criminal networks
• ISSN: 1573-3750; 1572-8315
• DOI: 10.1007/s11292-023-09563-z

Objectives Criminal networks tend to recover after a disruption, and this recovery may trigger negative unintended consequences by strengthening network cohesion. This study uses a real-world street gang network as a basis for simulating the effect of disruption and subsequent recovery on network structure. Methods This study utilises cohesion and centrality measures to describe the network and to simulate nine network disruptions. Stationary stochastic actor-oriented models are used to identify relational mechanisms in this network and subsequently to simulate network recovery in five scenarios. Results Removing the most central and the highest-ranking actors have the largest immediate impact on the network. In the long-term recovery simulation, networks become more compact (substantially so when increasing triadic closure), while the structure disintegrates when preferential attachment decreases. Conclusion These results indicate that the mechanisms driving network recovery are more important than the immediate impact of disruption due to network recovery.