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Illegal markets are notoriously difficult to study. Police data offer an increasingly exploited source of evidence. However, their secondary nature poses challenges for researchers. A key issue is that researchers often have to deal with two sets of actors: targeted and non-targeted. This work develops a latent space model for interdependent ego-networks purposely created to deal with the targeted nature of police evidence. By treating targeted offenders as egos and their contacts as alters, the model (a) leverages on the full information available and (b) mirrors the specificity of the data collection strategy. The paper then applies this approach to analyse a real-world example of illegal markets, namely the smuggling of migrants. To this end, we utilise a novel dataset of 21,555 phone conversations wiretapped by the police to study interactions among offenders.
Gollini, I., Caimo, A.,& Campana, P. (2020). Modelling interactions among offenders: a latent space approach for interdependent ego-networks.Social Networks, 63, pp.134– 149. doi:10.1016/j.socnet.2020.07.001