Disrupting resilient criminal networks through data analysis: The case of sicilian mafia

Compared to other types of social networks, criminal networks present particularly hard challenges, due to their strong resilience to disruption, which poses severe hurdles to LawEnforcement Agencies (LEAs). Herein, we borrow methods and tools from Social Network Analysis (SNA) to (i) unveil the structure and organization of Sicilian Mafia gangs, based on two real-world datasets, and (ii) gain insights as to how to efficiently reduce the Largest Connected Component (LCC) of two networks derived from them. Mafia networks have peculiar features in terms of the links distribution and strength, which makes them very different from other social networks, and extremely robust to exogenous perturbations. Analysts also face difficulties in collecting reliable datasets that accurately describe the gangs' internal structure and their relationships with the external world, which is why earlier studies are largely qualitative, elusive and incomplete. An added value of our work is the generation of two realworld datasets, based on raw data extracted from juridical acts, relating to a Mafia organization that operated in Sicily during the first decade of 2000s. We created two different networks, capturing phone calls and physical meetings, respectively. Our analysis simulated different intervention procedures: (i) arresting one criminal at a time (sequential node removal); and (ii) police raids (node block removal). In both the sequential, and the node block removal intervention procedures, the Betweenness centrality was the most effective strategy in prioritizing the nodes to be removed. For instance, when targeting the top 5% nodes with the largest Betweenness centrality, our simulations suggest a reduction of up to 70% in the size of the LCC. We also identified that, due the peculiar type of interactions in criminal networks (namely, the distribution of the interactions' frequency), no significant differences exist between weighted and unweighted network analysis. Our work has significant practical applications for perturbing the operations of criminal and terrorist networks

Criminal networks analysis in missing data scenarios through graph distances

Data collected in criminal investigations may suffer from issues like: (i) incompleteness, due to the covert nature of criminal organizations; (ii) incorrectness, caused by either unintentional data collection errors or intentional deception by criminals; (iii) inconsistency, when the same information is collected into law enforcement databases multiple times, or in different formats. In this paper we analyze nine real criminal networks of different nature (i.e., Mafia networks, criminal street gangs and terrorist organizations) in order to quantify the impact of incomplete data, and to determine which network type is most affected by it. The networks are firstly pruned using two specific methods: (i) random edge removal, simulating the scenario in which the Law Enforcement Agencies fail to intercept some calls, or to spot sporadic meetings among suspects; (ii) node removal, modeling the situation in which some suspects cannot be intercepted or investigated. Finally we compute spectral distances (i.e., Adjacency, Laplacian and normalized Laplacian Spectral Distances) and matrix distances (i.e., Root Euclidean Distance) between the complete and pruned networks, which we compare using statistical analysis. Our investigation identifies two main features: first, the overall understanding of the criminal networks remains high even with incomplete data on criminal interactions (i.e., when 10% of edges are removed); second, removing even a small fraction of suspects not investigated (i.e., 2% of nodes are removed) may lead to significant misinterpretation of the overall network. Copyright

Kaleidoscopic realities: Italian mafia(s) fiction’s audiences in argentina

Los escasos estudios realizados sobre las mafias italianas en Argentina señalan un fenómeno delimitado histórica y geográficamente. Las ficciones sobre las mafias parecen proporcionar el marco interpretativo básico para abordar este problema. Tienen el potencial de ayudar a generar un efecto de normalización de conductas, aceptación de grupos, estilos de vida, promoción de principios y modelos y posicionamiento social de personajes mafiosos que son de fundamental importancia para la difusión de la cultura mafiosa. ¿Cómo funciona este proceso a nivel transnacional? Para responder a esta pregunta, se presentarán los principales resultados de un experimento basado en la investigación empírica sobre audiencias.The few studies conducted on Italian mafias in Argentina pointed out to a historically and geographically delimited phenomenon. Mafias fictions seem to provide the basic interpretative scheme to approach this issue. Mafias dramas have the potential to contribute to generate an effect of normalisation of behaviors, acceptance of groups, lifestyles, promotion of principles and models, and social positioning of mafia characters that are of key importance for the dissemination of mafia cultures. How does this process actually work in a transnational basis? To address this question, the main findings of a two-step experiment based on empirical audience research will be presented.Fil: Balsas, María Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Centro de Investigaciones Sociales. Instituto de Desarrollo Económico y Social. Centro de Investigaciones Sociales; Argentin

The Secret Nexus. A case study of deviant masons, mafia, and corruption in Italy

This paper wishes to explore some characteristics of the relevant interconnections between mafias/mafiosi and masonic lodges/masons in the Italian context. The paper sets out to study these interconnections from a social science perspective rooted in sociological and neo-institutional studies of organised crime and mafias, but also in criminological approaches to social constructionism, in the form of symbols and narratives. We will present a case study to reflect on the roles that (deviant) masons can assume in contexts where both mafias’ and personal, political, or economic interests are at play. The case study shows how masonic alliances can augment networking and enforcing capabilities: we call this process masonic deviance amplification. Additionally, the case study confirms the constitutive power that narratives around the masonic world hold today in the Italian context

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.N/

Disrupting resilient criminal networks through data analysis: The case of Sicilian Mafia

Compared to other types of social networks, criminal networks present particularly hard challenges, due to their strong resilience to disruption, which poses severe hurdles to Law-Enforcement Agencies (LEAs). Herein, we borrow methods and tools from Social Network Analysis (SNA) to (i) unveil the structure and organization of Sicilian Mafia gangs, based on two real-world datasets, and (ii) gain insights as to how to efficiently reduce the Largest Connected Component (LCC) of two networks derived from them. Mafia networks have peculiar features in terms of the links distribution and strength, which makes them very different from other social networks, and extremely robust to exogenous perturbations. Analysts also face difficulties in collecting reliable datasets that accurately describe the gangs’ internal structure and their relationships with the external world, which is why earlier studies are largely qualitative, elusive and incomplete. An added value of our work is the generation of two real-world datasets, based on raw data extracted from juridical acts, relating to a Mafia organization that operated in Sicily during the first decade of 2000s. We created two different networks, capturing phone calls and physical meetings, respectively. Our analysis simulated different intervention procedures: (i) arresting one criminal at a time (sequential node removal); and (ii) police raids (node block removal). In both the sequential, and the node block removal intervention procedures, the Betweenness centrality was the most effective strategy in prioritizing the nodes to be removed. For instance, when targeting the top 5% nodes with the largest Betweenness centrality, our simulations suggest a reduction of up to 70% in the size of the LCC. We also identified that, due the peculiar type of interactions in criminal networks (namely, the distribution of the interactions’ frequency), no significant differences exist between weighted and unweighted network analysis. Our work has significant practical applications for perturbing the operations of criminal and terrorist networks

Part 5: Organized Criminal Activities

This part contains references to literature that examine the activities associated with criminal organizations. This includes both strategic (i.e., profit-oriented ventures such as drug trafficking, fraud, counterfeiting, prostitution, etc.) and tactical activities (i.e., activities that support the criminal organization and its profit-oriented activities, such as money laundering, violence, corruption, etc.) References are provided for the criminal activities listed below. Within each category are also references to publications that address the control of a particular criminal activity (e.g., enforcement, prevention, laws)

Criminal networks analysis in missing data scenarios through graph distances

Data collected in criminal investigations may suffer from issues like: (i) incompleteness, due to the covert nature of criminal organizations; (ii) incorrectness, caused by either unintentional data collection errors or intentional deception by criminals; (iii) inconsistency, when the same information is collected into law enforcement databases multiple times, or in different formats. In this paper we analyze nine real criminal networks of different nature (i.e., Mafia networks, criminal street gangs and terrorist organizations) in order to quantify the impact of incomplete data, and to determine which network type is most affected by it. The networks are firstly pruned using two specific methods: (i) random edge removal, simulating the scenario in which the Law Enforcement Agencies fail to intercept some calls, or to spot sporadic meetings among suspects; (ii) node removal, modeling the situation in which some suspects cannot be intercepted or investigated. Finally we compute spectral distances (i.e., Adjacency, Laplacian and normalized Laplacian Spectral Distances) and matrix distances (i.e., Root Euclidean Distance) between the complete and pruned networks, which we compare using statistical analysis. Our investigation identifies two main features: first, the overall understanding of the criminal networks remains high even with incomplete data on criminal interactions (i.e., when 10% of edges are removed); second, removing even a small fraction of suspects not investigated (i.e., 2% of nodes are removed) may lead to significant misinterpretation of the overall network.Libera Università di Bolzan

Territorial capital as a source of firm competitive advantage: evidence from the North and South of Italy

This paper investigates how territorial capital, defined as a mix of tangible and intangible local resources accumulated over time across different territories, becomes a source of competitive advantage for firms. The study draws upon semi-structured interviews with firms’ owner-managers operating in the North and South of Italy and shows how local resources generate firms’ costs and differentiation advantages through acting as territorial externalities or becoming an essential core asset to the firm. Results demonstrate how local resources are highly interconnected, making territorial capital unique in each place and not easily imitable, which ensures long term competitive advantages for those firms that benefit from its endowment. A mix of advanced local resources developed through long term investment is shown to be more valuable for firms than inherited resources, provided by ‘God’ or ‘ancestors’. Using the concept of territorial capital in this manner provides insights into understanding sources of firm competitiveness related to location and the persistence of territorial economic disparities