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Machine learning partners in criminal networks.

Diego D Lopes1, Bruno R da Cunha2,3, Alvaro F Martins1

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Analyzing criminal networks reveals structural patterns that predict illegal activities. Machine learning accurately identifies missing links, differentiates associations, and forecasts future criminal behavior in corruption and money laundering networks.

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Area of Science:

  • Network Science
  • Machine Learning
  • Criminology

Background:

  • Criminal networks exhibit complex organizational structures.
  • Predictive capabilities of these structures for network properties are underexplored.

Purpose of the Study:

  • To investigate if structural properties of criminal networks can predict static and dynamic attributes.
  • To apply graph representation learning and machine learning for analyzing criminal networks.

Main Methods:

  • Combined graph representation learning and machine learning.
  • Analyzed structural properties of political corruption, police intelligence, and money laundering networks.

Main Results:

  • Accurately recovered missing criminal partnerships.
  • Successfully distinguished between criminal and legal associations.
  • Predicted money exchange amounts and anticipated future criminal associations in growing corruption networks.

Conclusions:

  • Structural patterns in criminal networks contain vital information about illegal activities.
  • Machine learning methods can leverage these patterns for prediction and forecasting of criminal behavior.