Universality of political corruption networks
Alvaro F. Martins,Bruno Requião da Cunha,Quentin S. Hanley,Sebastián Gonçalves,Matjaž Perc,Haroldo V. Ribeiro +5 more
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TLDR
In this paper , a comprehensive investigation of corruption networks related to political scandals in Spain and Brazil over nearly three decades is presented, showing that corruption networks of both countries share universal structural and dynamical properties, including similar degree distributions, clustering and assortativity coefficients, modular structure, and a growth process that is marked by the coalescence of network components due to a few recidivist criminals.Abstract:
Corruption crimes demand highly coordinated actions among criminal agents to succeed. But research dedicated to corruption networks is still in its infancy and indeed little is known about the properties of these networks. Here we present a comprehensive investigation of corruption networks related to political scandals in Spain and Brazil over nearly three decades. We show that corruption networks of both countries share universal structural and dynamical properties, including similar degree distributions, clustering and assortativity coefficients, modular structure, and a growth process that is marked by the coalescence of network components due to a few recidivist criminals. We propose a simple model that not only reproduces these empirical properties but reveals also that corruption networks operate near a critical recidivism rate below which the network is entirely fragmented and above which it is overly connected. Our research thus indicates that actions focused on decreasing corruption recidivism may substantially mitigate this type of organized crime. read more
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Machine learning partners in criminal networks
Diego D. Lopes,Bruno Requião da Cunha,Alvaro F. Martins,Sebastián Gonçalves,Ervin K. Lenzi,Quentin S. Hanley,Matjaž Perc,Haroldo V. Ribeiro +7 more
TL;DR: In this paper , structural properties of political corruption, police intelligence, and money laundering networks can be used to recover missing criminal partnerships, distinguish among different types of criminal and legal associations, as well as predict the total amount of money exchanged among criminal agents.
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Mathematical Model Analysis on the Diffusion of Violence
TL;DR: In this article , a firsthand violence mathematical model with five distinct classes of the human population (susceptible, violence-exposed, violence, negotiated, and reconciled) is presented.
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Modeling the role of police corruption in the reduction of organized crime: Mexico as a case study
TL;DR: In this article , a simple agent-based model was used to analyze the effect of both corruption and perception of corruption within law-enforcement corporations on crime rates and found that one of the parameters that strongly controls crime incidence is the probability that regular citizens become criminals.
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A natural history of networks: Modeling higher-order interactions in geohistorical data
Alexi José Rojas,Anton Holmgren,Magnus Neuman,Daniel Edler,Christopher Blöcker,Martin Rosvall +5 more
TL;DR: In this paper , the Map Equation framework is used for higher-order models of geohistorical data, addressing some practical decisions involved in modeling complex dependencies, and discuss critical methodological and conceptual issues that make it difficult to compare results across studies in the growing body of network paleobiology research.
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Deep Learning Criminal Networks
Haroldo V. Ribeiro,Diego D. Lopes,Arthur A. B. Pessa,Alvaro F. Martins,Bruno Requião da Cunha,Ervin K. Lenzi,Quentin S. Hanley,Matjaž Perc +7 more
TL;DR: In this article , the authors explore the potential of graph convolutional networks to learn patterns among networked criminals and to predict various properties of criminal networks, including missing criminal partnerships, distinguish among types of associations, predict the amount of money exchanged among criminal agents, and even anticipate partnerships and recidivism of criminals during the growth dynamics of corruption networks.
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