Social network analysis: the use of graph distances to compare artificial and criminal networks

Aim: Italian criminal groups become more and more dangerous spreading their activities into new sectors. A criminal group is made up of networks of hundreds of family gangs which extended their influence across the world, raking in billions from drug trafficking, extortion and money laundering. We focus in particular on the analysis of the social structure of two Sicilian crime families and we used a Social Network Analysis approach to study the social phenomena. Starting from a real criminal network extracted from meetings emerging from the police physical surveillance during 2000s, we here aim to create artificial models that present similar properties. Methods: We use specific tools of social network analysis and graph theory such as network models (i.e., random, small-world and scale-free) and graph distances to quantify the similarity between an artificial network and a real one. To the best of our knowledge, spectral graph distances and the DeltaCon similarity have never been applied to criminal networks. Results: Our experiments identify the Barabási-Albert model as the one which better represents a criminal network. For this reason, we could expect that new members of a criminal organization will be more likely to establish connections with high degree nodes rather than low degree nodes. Conclusion: Artificial but realistic models can represent a useful tool for Law Enforcement Agencies to simulate and study the structure, evolution and faults of criminal networks.