activity
20182025
most citedMachine Learning Partners in Criminal Networks

41 citations · 87 across the 4 of their papers we have counts for

collaborators

5 papers

physics.soc-ph2025★ 4 cited

Structural roles and gender disparities in corruption networks

Arthur A. B. Pessa, Alvaro F. Martins, Monica V. Prates +4

Criminal activities are predominantly due to males, with females exhibiting a significantly lower involvement, especially in serious offenses. This pattern extends to organized cri…

physics.soc-ph2023★ 41 cited

Deep Learning Criminal Networks

Haroldo V. Ribeiro, Diego D. Lopes, Arthur A. B. Pessa +6

Recent advances in deep learning methods have enabled researchers to develop and apply algorithms for the analysis and modeling of complex networks. These advances have sparked a s…

physics.soc-ph2022★ 41 cited

Machine Learning Partners in Criminal Networks

Diego D. Lopes, Bruno R. da Cunha, Alvaro F. Martins +5

Recent research has shown that criminal networks have complex organizational structures, but whether this can be used to predict static and dynamic properties of criminal networks…

physics.soc-ph2022★ 1 cited

Universality of political corruption networks

Alvaro F. Martins, Bruno R. da Cunha, Quentin S. Hanley +3

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 kno…

physics.soc-ph2018

The dynamical structure of political corruption networks

Haroldo V. Ribeiro, Luiz G. A. Alves, Alvaro F. Martins +2

Corruptive behaviour in politics limits economic growth, embezzles public funds, and promotes socio-economic inequality in modern democracies. We analyse well-documented political…