The problem of link prediction has recently received increasing attention from scholars in network science. In social network analysis, one of its aims is to recover missing links, namely connections among actors which are likely to exist but have not been reported because data are incomplete or subject to various types of uncertainty. In the field of criminal investigations, problems of incomplete information are encountered almost by definition, given the obvious anti-detection strategies set up by criminals and the limited investigative resources. In this paper, we work on a specific dataset obtained from a real investigation, and we propose a strategy to identify missing links in a criminal network on the basis of the topological analysis of the links classified as marginal, i.e. removed during the investigation procedure. The main assumption is that missing links should have opposite features with respect to marginal ones. Measures of node similarity turn out to provide the best characterization in this sense. The inspection of the judicial source documents confirms that the predicted links, in most instances, do relate actors with large likelihood of co-participation in illicit activities.

Illicit drugs are trafficked across manifold borders before ultimately reaching consumers. Consequently, interdiction of cross-border drug trafficking forms a critical component of the European Union's initiative to reduce drug supplies. However, there is contradictory evidence about its effectiveness, which is due, in part, to a paucity of information about how drugs flow across borders. This study uses a network approach to analyze international drug trafficking both to and within Europe, drawing on several perspectives to delineate the factors that affect how drug shipments move across borders. The analysis explicates how drug trafficking is concentrated along specific routes; moreover, we demonstrate that its structure is not random but, rather, driven by specific factors. In particular, corruption and social and geographical proximity are key factors explaining the configuration of heroin supply to European countries. This study also provides essential insights into the disruption of traffickers' illicit activities.

Law enforcement agencies rely on data collected from wire taps to construct the organizational chart of criminal enterprises. Recently, a number of academics have also begun to utilise social network analysis to describe relations among criminals and understand the internal organisation of criminal groups. However, before drawing conclusions about the structure or the organisation of criminal groups, it is important to understand the limitations that selective samples such as wire taps may have on network analysis measures. Electronic surveillance data can be found in different kinds of court records and the selection of the data source is likely to influence the amount of missing information and, consequently, the results. This article discusses the impact that the selection of a specific data source for the social network analysis of criminal groups may have on centrality measures usually adopted in organised crime research to identify key players.

This study complements existing literature on the mobility of criminal groups (mainly based on country case studies) with the first systematic assessment of the worldwide activities of the four main types of Italian mafias (Cosa Nostra, Camorra, 'Ndrangheta and Apulian mafias) from 2000 to 2012. Drawing from publicly available reports, a specific multiple correspondence analysis identifies the most important associations among mafias, activities, and countries. The results show that the mafias concentrate in a few countries; drug trafficking is the most frequent activity, whereas money laundering appears less important than expected; a stable mafia presence is reported in a few developed countries (mainly Germany, Canada, Australia, and the United States). The mafias show significant differences: the 'Ndrangheta tends to establish structured groups abroad, whereas the other mafias mainly participate in illicit trades.

Illegal enterprise and social embeddedness theories have highlighted the importance of market forces and social factors, respectively, for analyzing organized crime and organized criminal activities. This paper empirically demonstrates the joint explanatory power of these respective theories in the case of the transnational trafficking of cocaine. It does so by conceptualizing transnational cocaine trafficking as a network of relationships among countries; a network whose structure reflects the actions of manifold organized criminal groups. The analysis utilizes exponential random graph models to analyze quantitative data on cocaine trafficking which are ordinarily difficult to capture in empirical research. The analysis presented focuses on a set of 36 European countries. The results yield insights into the nature of the relationship among economic incentives, social ties, geographic features and corruption, and how, in turn, this relationship influences the structure of the transnational cocaine network and the modi operandi of cocaine traffickers.

Mafia homicides are usually committed for retaliation, economic profit, or rivalry among groups. The variety of possible reasons suggests the inefficacy of a preventive approach. However, like most violent crimes, mafia homicides concentrate in space due to place-specific social and environmental features. Starting from the existing literature, this study applies the Risk Terrain Modeling approach to forecast the Camorra homicides in Naples, Italy. This approach is based on the identification and evaluation of the underlying risk factors able to affect the risk of a homicide. This information is then used to predict the most likely location of future events. The findings of this study demonstrate that past homicides, drug dealing, confiscated assets, and rivalries among groups make it possible to predict up to 85% of 2012 mafia homicides, identifying 11% of city areas at highest risk. By contrast, variables controlling for the socio-economic conditions of areas are not significantly related to the risk of homicide. Moreover, this study shows that, even in a restricted space, the same risk factors may combine in different ways, giving rise to areas of equal risk but requiring targeted remedies. These results provide an effective basis for short-and long-term targeted policing strategies against organized crime-and gang-related violence. A similar approach may also provide practitioners, policy makers, and local administrators in other countries with significant support in understanding and counteracting also other forms of violent behavior by gangs or organized crime groups.

This study utilises recent advances in statistical models for social networks to identify the factors shaping heroin trafficking in relation to European countries. First, it estimates the size of the heroin flows among a network of 61 countries, before subsequently using a latent space approach to model the presence of trafficking and the amount of heroin traded between any two given countries. Many networks, such as trade networks, are intrinsically weighted, and ignoring edge weights results in a loss of relevant information. Traditionally, the gravity model has been used to predict legal trade flows, assuming conditional independence among observations. More recently, latent space position models for social networks have been used to analyze legal trade among countries, and, mutatis mutandis, can be applied to the context of illegal trade to count both edge weights and conditional dependence among observations. These models allow for a better understanding of the generative processes and potential evolution of heroin trafficking routes. This study shows that geographical and social proximity provide fertile ground for the formation of heroin flows. Opportunities are also a driver of drug flows towards countries where regulation of corruption is weak

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