| Naslov: | Machine learning partners in criminal networks |
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| Avtorji: | ID Lopes, Diego D. (Avtor)
ID Cunha, Bruno R. da (Avtor)
ID Martins, Alvaro F. (Avtor)
ID Gonçalves, Sebastián (Avtor)
ID Lenzi, Ervin K. (Avtor)
ID Hanley, Quentin S. (Avtor)
ID Perc, Matjaž (Avtor)
ID Ribeiro, Haroldo V. (Avtor) |
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| Datoteke: |  Lopes-2022-Machine_learning_partners_in_crimin.pdf (2,42 MB)
MD5: A043AB12AAE0819F8D1D5C4A09A12EA8
 https://doi.org/10.1038/s41598-022-20025-w
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| Jezik: | Angleški jezik |
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| Vrsta gradiva: | Znanstveno delo |
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| Tipologija: | 1.01 - Izvirni znanstveni članek |
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| Organizacija: | FNM - Fakulteta za naravoslovje in matematiko
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| Opis: | 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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| Ključne besede: | machine learning, crime, network, social physics |
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| Status publikacije: | Objavljeno |
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| Verzija publikacije: | Objavljena publikacija |
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| Poslano v recenzijo: | 03.05.2022 |
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| Datum sprejetja članka: | 07.09.2022 |
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| Datum objave: | 21.09.2022 |
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| Založnik: | Nature Publishing Group |
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| Leto izida: | 2022 |
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| Št. strani: | Str. 1-9 |
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| Številčenje: | Letn. 12, Št. članka 15746 |
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| PID: | 20.500.12556/DKUM-88735-334e8c84-84aa-3395-f9a3-555832947098  |
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| UDK: | 53 |
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| COBISS.SI-ID: | 123688707 |
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| DOI: | 10.1038/s41598-022-20025-w |
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| ISSN pri članku: | 2045-2322 |
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| Datum objave v DKUM: | 28.05.2024 |
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| Število ogledov: | 867 |
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| Število prenosov: | 15 |
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| Metapodatki: |    |
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| Področja: | Ostalo
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