Machine learning partners in criminal networks

Lopes, D.D., da Cunha, B.R., Martins, A.F., Gonçalves, S., Lenzi, E.K., Hanley, Q. ORCID: 0000-0002-8189-9550, Perc, M. and Ribeiro, H.V., 2022. Machine learning partners in criminal networks. Scientific Reports, 12: 15746.

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Abstract

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 remains little explored. Here, by combining graph representation learning and machine learning methods, we show that 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, all with outstanding accuracy. We also show that our approach can anticipate future criminal associations during the dynamic growth of corruption networks with significant accuracy. Thus, similar to evidence found at crime scenes, we conclude that structural patterns of criminal networks carry crucial information about illegal activities, which allows machine learning methods to predict missing information and even anticipate future criminal behavior.

Item Type:	Journal article
Publication Title:	Scientific Reports
Creators:	Lopes, D.D., da Cunha, B.R., Martins, A.F., Gonçalves, S., Lenzi, E.K., Hanley, Q., Perc, M. and Ribeiro, H.V.
Publisher:	Nature Research (part of Springer Nature)
Date:	2022
Volume:	12
Identifiers:	Number Type 10.1038/s41598-022-20025-w DOI 1599856 Other
Rights:	© The Author(s) 2022. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Divisions:	Schools > School of Science and Technology
Record created by:	Linda Sullivan
Date Added:	22 Sep 2022 08:48
Last Modified:	22 Sep 2022 08:48
URI:	https://irep.ntu.ac.uk/id/eprint/47070

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