Structural Resilience and Recovery of a Criminal Network After Disruption: A simulation study

By Tomáš Diviák 

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.

Journal of Experimental Criminology (2023)