Document Type
Article
Rights
This item is available under a Creative Commons License for non-commercial use only
Disciplines
Statistics
Abstract
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.
DOI
https://doi.org/10.1016/j.socnet.2020.07.001
Recommended Citation
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
Included in
Applied Statistics Commons, Mathematics Commons, Social Statistics Commons, Statistical Models Commons
Publication Details
Social Networks