Criminal networks are different from any other networks studied in science. No other networks operate under the same objectives, efficiency-security trade-offs and adversarial nature that characterize criminal contexts. These distinctive aspects generate peculiar forms of incompleteness and structural complexity within the available criminal network data, which hinder progress in understanding how illicit organizations form, function, evolve and persist. While standard techniques from social network analysis have achieved important advances in this area, as leading scholars have noted, criminal networks are not simply social networks in criminal contexts.
NEMESIS embraces this challenge and transforms it into a unique opportunity to substantially advance the state of the art in criminal network analysis and modeling. Rather than viewing incomplete and partially-observed data as a limitation, the project interprets data missingness as a meaningful structural information that can reveal efficiency-security trade-offs, strategic redundancies, resilience mechanisms and obscured organizational boundaries. At the same time, it leverages the complexity inherent in observed criminal network data to infer higher-level topologies that uncover the sophisticated generative structures underlying organized crime. These include tree-based nested modular hierarchies among criminals, systematic architectures shared across multiplex interactions, and adaptive growth mechanisms. Collectively, these objectives aim to develop innovative analytical perspectives capable of formally capturing the mechanisms that shape the structure, evolution and resilience of criminal networks, ultimately supporting the design of more effective law-enforcement interventions.
To achieve the above objectives, NEMESIS integrates modern criminological and social science theories with rigorous statistical methodology and advanced computational techniques. The project designs innovative models for multiplex, multilayer and dynamic networks, combining evolutionary tree representations, zero-inflated processes, dependent partition structures and latent variable approaches. This integrated perspective expands current approaches to criminal network analysis and generates methodological innovations with relevance even beyond criminology. Its expected impact lies in advancing scientific understanding of organized crime and providing rigorous analytical foundations for more informed policy responses.
