| Title: | Machine learning partners in criminal networks |
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| Authors: | ID Lopes, Diego D. (Author)
ID Cunha, Bruno R. da (Author)
ID Martins, Alvaro F. (Author)
ID Gonçalves, Sebastián (Author)
ID Lenzi, Ervin K. (Author)
ID Hanley, Quentin S. (Author)
ID Perc, Matjaž (Author)
ID Ribeiro, Haroldo V. (Author) |
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| Files: |  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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| Language: | English |
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| Work type: | Scientific work |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FNM - Faculty of Natural Sciences and Mathematics
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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 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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| Keywords: | machine learning, crime, network, social physics |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 03.05.2022 |
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| Article acceptance date: | 07.09.2022 |
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| Publication date: | 21.09.2022 |
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| Publisher: | Nature Publishing Group |
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| Year of publishing: | 2022 |
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| Number of pages: | Str. 1-9 |
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| Numbering: | Letn. 12, Št. članka 15746 |
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| PID: | 20.500.12556/DKUM-88735-334e8c84-84aa-3395-f9a3-555832947098  |
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| UDC: | 53 |
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| ISSN on article: | 2045-2322 |
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| COBISS.SI-ID: | 123688707 |
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| DOI: | 10.1038/s41598-022-20025-w |
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| Publication date in DKUM: | 28.05.2024 |
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| Views: | 869 |
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| Downloads: | 15 |
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| Metadata: |    |
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| Categories: | Misc.
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