Energy Disruptive Centrality with an Application to Criminal Network

2021 ◽  
pp. 105834
Author(s):  
Ricardo Lopes de Andrade ◽  
Leandro Chaves Rêgo ◽  
Ticiana L. Coelho da Silva ◽  
José Antônio F. de Macêdo ◽  
Wellington C.P. Silva
Keyword(s):  
Criminal Network

scholarly journals Social ties between criminal networks in cocaine trafficking in Europe

Crimen ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 325-345
Author(s):  
Kosara Stevanović
Keyword(s):  
Latin American ◽  
Organized Crime ◽  
Network Theory ◽  
Social Ties ◽  
Social Embeddedness ◽  
Criminal Networks ◽  
Criminal Network ◽  
The Social ◽  
Criminal Groups ◽  
In The Beginning

This paper is highlighting the main criminal networks that are trafficking cocaine in Europe, through the lenses of social embeddedness and criminal network theories. We will try to show that social ties between European and Latin American organized crime networks, as well as between different European crime networks, are the main reason for the staggering success of European criminal groups in cocaine trafficking in the 21st century. In the beginning, we lay out the social embeddedness theory and criminal network theory, and then we review the main criminal networks involved in cocaine trafficking in Europe and social ties between them, with special attention to Serbian and Montenegrin criminal networks. At the end of the article, we analyze what role does ethnicity, seen as social ties based on common language and tradition, play in cocaine trafficking in Europe.

Facilitating Irregular Migration into Malaysia and from Indonesia: Illicit Markets, Endemic Corruption and Symbolic Attempts to Overcome Impunity

2021 ◽  
Vol 3 (1) ◽  
pp. 8-31
Author(s):  
Gerhard Hoffstaedter ◽  
Antje Missbach
Keyword(s):  
Law Enforcement ◽  
Case Studies ◽  
Organised Crime ◽  
The State ◽  
Income Generation ◽  
Irregular Migration ◽  
Criminal Network ◽  
Control Power ◽  
Illicit Markets ◽  
Formal Economy

Abstract Discourses around illicit markets for irregular migration focus on criminality and global dimensions threatening border security and the sovereignty of the state. Organised crime has generally been understood to be committed by crime syndicates outside or parallel to the dominant order and formal economy. In Malaysia and Indonesia, however, the state (or parts thereof) is heavily implicated in such crime and essential for the success of unsanctioned trans-border movements. The participation of state officials could be analysed as a convergence of extralegal income generation and symbolic law enforcement. This article presents case studies from Malaysia and Indonesia that could only have taken place because security officials facilitated them. It challenges the orthodoxy of a state versus criminal network opposition and seeks to explain the circumstances under which legal prosecution occurs. The symbolic punishment of low-ranking officials reinforces networks of control, power hierarchies and cooperation of the state in illicit markets.

scholarly journals Exogenous Shocks, the Criminal Elite, and Increasing Gender Inequality in Chicago Organized Crime

2020 ◽  
Vol 85 (5) ◽  
pp. 895-923
Author(s):  
Chris M. Smith
Keyword(s):  
Gender Inequality ◽  
Relational Database ◽  
Organized Crime ◽  
Organizational Restructuring ◽  
Historical Case ◽  
Exogenous Shock ◽  
Criminal Network ◽  
Exogenous Shocks ◽  
Criminal Organizations ◽  
The U.S

Criminal organizations, like legitimate organizations, adapt to shifts in markets, competition, regulations, and enforcement. Exogenous shocks can be consequential moments of power consolidation, resource hoarding, and inequality amplification in legitimate organizations, but especially in criminal organizations. This research examines how the exogenous shock of the U.S. prohibition of the production, transportation, and sale of alcohol in 1920 restructured power and inequality in Chicago organized crime. I analyze a unique relational database on organized crime from the early 1900s via a criminal network that tripled in size and centralized during Prohibition. Before Prohibition, Chicago organized crime was small, decentralized, and somewhat inclusive of women at the margins. However, during Prohibition, the organized crime network grew, consolidated the organizational elites, and left out the most vulnerable participants from the most profitable opportunities. This historical case illuminates how profits and organizational restructuring outside of (or in response to) regulatory environments can displace people at the margins.

scholarly journals Hidden Link Prediction in Criminal Networks Using the Deep Reinforcement Learning Technique

Computers ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 8 ◽  
Author(s):  
Marcus Lim ◽  
Azween Abdullah ◽  
NZ Jhanjhi ◽  
Mahadevan Supramaniam
Keyword(s):  
Machine Learning ◽  
Reinforcement Learning ◽  
Network Analysis ◽  
Prediction Model ◽  
Supervised Learning ◽  
Link Prediction ◽  
Supervised Machine Learning ◽  
Criminal Networks ◽  
Criminal Network ◽  
Learning Technique

Criminal network activities, which are usually secret and stealthy, present certain difficulties in conducting criminal network analysis (CNA) because of the lack of complete datasets. The collection of criminal activities data in these networks tends to be incomplete and inconsistent, which is reflected structurally in the criminal network in the form of missing nodes (actors) and links (relationships). Criminal networks are commonly analyzed using social network analysis (SNA) models. Most machine learning techniques that rely on the metrics of SNA models in the development of hidden or missing link prediction models utilize supervised learning. However, supervised learning usually requires the availability of a large dataset to train the link prediction model in order to achieve an optimum performance level. Therefore, this research is conducted to explore the application of deep reinforcement learning (DRL) in developing a criminal network hidden links prediction model from the reconstruction of a corrupted criminal network dataset. The experiment conducted on the model indicates that the dataset generated by the DRL model through self-play or self-simulation can be used to train the link prediction model. The DRL link prediction model exhibits a better performance than a conventional supervised machine learning technique, such as the gradient boosting machine (GBM) trained with a relatively smaller domain dataset.

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