NEMESIS

Criminal networks are different than any other network studied in science, since no other network has the objectives, trade–offs and adversarial nature of those generated in criminal contexts. This uniqueness yields peculiar forms of data incompleteness and complexity which undermine advancements in the analysis of modern criminal networks. While standard techniques from social network analysis can improve the picture, quoting leading scholars in the field, "criminal networks are not simply social networks in criminal contexts".

NEMESIS embraces this fascinating challenge and transforms it into unexplored opportunities to develop impactful modeling perspectives which combine [1] modern theories from Criminology and Social Sciences, [2] rigorous and innovative methodology from Statistics, and [3] efficient computational techniques to expand current studies of criminal networks. From a methodological perspective, NEMESIS will design state–of–the–art models (with impact even beyond criminology) for multiplex, multilayer and dynamic networks, relying on yet unexplored combinations of evolutionary trees, zero–inflated processes, dependent random partitions, latent variable representations, and other advanced constructions capable of formally incorporating and inferring generative structures specific to criminal networks, while accounting for the unique research questions and modern theories provided by Criminology and Social Sciences.

The project will be carried out within the Bocconi Institute for Data Science and Analytics [BIDSA], a vibrant research center which promotes and facilitates data–driven research at Bocconi University. It represents Bocconi's timely answer to the increasing interest in Data Science across all fields. The abundance and complexity of large–scale datasets in science and every–day life is creating exciting opportunities and great challenges for our society. In order to exploit the information contained in these datasets and model the underlying complex phenomena, modern data scientists need to combine advanced knowledge from a variety of disciplines, including mathematics, statistics, computer science, social sciences and other domain–specific knowledge. By acting as a central hub of data science research and education at Bocconi, BIDSA allows for effective interdisciplinary collaborations and cross–fertilization across data–related research areas.