Outlier mining in criminal networks: the role of machine learning and outlier detection models

Alex S. O. Toledo (), Laura C. Carpi (), Allbens P. F. Atman () and A. P. Baêta Scarpelli ()
Additional contact information
Alex S. O. Toledo: Centro Federal de Educação Tecnológica de Minas Gerais
Laura C. Carpi: Centro Federal de Educação Tecnológica de Minas Gerais
Allbens P. F. Atman: Centro Federal de Educação Tecnológica de Minas Gerais
A. P. Baêta Scarpelli: Centro Federal de Educação Tecnológica de Minas Gerais

Journal of Computational Social Science, 2025, vol. 8, issue 2, No 10, 22 pages

Abstract: Abstract This study explores the identification and disruption of criminal networks through an innovative approach that combines complex network theory, machine learning, and outlier detection models. Using real data from criminal records provided by the Military Police of Minas Gerais (Brazil), advanced data cleaning and processing techniques were applied, resulting in a robust set of variables that describe interactions within criminal networks. Six outlier detection models were evaluated, including a normalized Euclidean distance-based score, a Jensen-Shannon divergence-based score, Isolation Forest, Local Outlier Factor, Robust Covariance, and SGD One-Class Support Vector Machine. These models were assessed to identify key agents in three disruption approaches: human, social, and mixed capital. The normalized Euclidean distance-based score, which was applied to the social capital approach, proved the most effective, increasing the number of components, reducing network efficiency, and fragmenting the largest connected components. The results demonstrate that the combination of complex networks and outlier detection offers a promising and effective strategy to disrupt criminal networks, emphasizing the importance of interdisciplinary collaboration and advanced technologies in public safety.

Keywords: Outlier mining; Criminal networks; Machine learning; Outlier detection; Complexity networks (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s42001-025-00364-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jcsosc:v:8:y:2025:i:2:d:10.1007_s42001-025-00364-0

Ordering information: This journal article can be ordered from
http://www.springer. ... iences/journal/42001

DOI: 10.1007/s42001-025-00364-0

Access Statistics for this article

Journal of Computational Social Science is currently edited by Takashi Kamihigashi

More articles in Journal of Computational Social Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().