Machine learning partners in criminal networks
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/253169 |
Resumo: | 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 diferent 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 signifcant 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. |
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Lopes, Diego DominguesCunha, Bruno Requião daMartins, Alvaro F.Goncalves, SebastianLenzi, Ervin KaminskiHanley, Quentin S.Perc, MatjažRibeiro, Haroldo Valentin2022-12-24T05:05:28Z20222045-2322http://hdl.handle.net/10183/253169001155975Recent 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 diferent 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 signifcant 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.application/pdfengScientific reports. London. Vol. 12 (Sept. 2022), 6858, 9 p.Corrupção políticaCrime organizadoEscândalo políticoMachine learning partners in criminal networksEstrangeiroinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSTEXT001155975.pdf.txt001155975.pdf.txtExtracted Texttext/plain47531http://www.lume.ufrgs.br/bitstream/10183/253169/2/001155975.pdf.txte6f3944c79bdc6ec13ce962ef9e97360MD52ORIGINAL001155975.pdfTexto completo (inglês)application/pdf2529081http://www.lume.ufrgs.br/bitstream/10183/253169/1/001155975.pdf199c2baf464f3988d2b44766eea9d9dfMD5110183/2531692024-05-19 05:46:20.767503oai:www.lume.ufrgs.br:10183/253169Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2024-05-19T08:46:20Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
Machine learning partners in criminal networks |
title |
Machine learning partners in criminal networks |
spellingShingle |
Machine learning partners in criminal networks Lopes, Diego Domingues Corrupção política Crime organizado Escândalo político |
title_short |
Machine learning partners in criminal networks |
title_full |
Machine learning partners in criminal networks |
title_fullStr |
Machine learning partners in criminal networks |
title_full_unstemmed |
Machine learning partners in criminal networks |
title_sort |
Machine learning partners in criminal networks |
author |
Lopes, Diego Domingues |
author_facet |
Lopes, Diego Domingues Cunha, Bruno Requião da Martins, Alvaro F. Goncalves, Sebastian Lenzi, Ervin Kaminski Hanley, Quentin S. Perc, Matjaž Ribeiro, Haroldo Valentin |
author_role |
author |
author2 |
Cunha, Bruno Requião da Martins, Alvaro F. Goncalves, Sebastian Lenzi, Ervin Kaminski Hanley, Quentin S. Perc, Matjaž Ribeiro, Haroldo Valentin |
author2_role |
author author author author author author author |
dc.contributor.author.fl_str_mv |
Lopes, Diego Domingues Cunha, Bruno Requião da Martins, Alvaro F. Goncalves, Sebastian Lenzi, Ervin Kaminski Hanley, Quentin S. Perc, Matjaž Ribeiro, Haroldo Valentin |
dc.subject.por.fl_str_mv |
Corrupção política Crime organizado Escândalo político |
topic |
Corrupção política Crime organizado Escândalo político |
description |
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 diferent 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 signifcant 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. |
publishDate |
2022 |
dc.date.accessioned.fl_str_mv |
2022-12-24T05:05:28Z |
dc.date.issued.fl_str_mv |
2022 |
dc.type.driver.fl_str_mv |
Estrangeiro info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
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publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10183/253169 |
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2045-2322 |
dc.identifier.nrb.pt_BR.fl_str_mv |
001155975 |
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2045-2322 001155975 |
url |
http://hdl.handle.net/10183/253169 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.pt_BR.fl_str_mv |
Scientific reports. London. Vol. 12 (Sept. 2022), 6858, 9 p. |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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