Обнаружение нелегальных транзакций биткойна при помощи графовых нейронных сетей; Перспективы развития фундаментальных наук; Т. 3 : Математика
| Parent link: | Курзина, И. А. (химик ; 1972-). Перспективы развития фундаментальных наук=Prospects of Fundamental Sciences Development: сборник научных трудов XX Международной конференции студентов, аспирантов и молодых ученых, г. Томск, 25-28 апреля 2023 г..— .— Томск: Изд-во ТПУ, 2023 Т. 3 : Математика.— 2023.— С. 15-17 |
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| Riassunto: | Заглавие с экрана This article discusses possible methods for solving the problem of classifying bitcoin transactions into licit and illicit ones. The proposed options for solving this problem of anti-money laundering analytics based on graph neural networks with different architecture. The results of computational experiments using data from the Elliptic Data Set are presented. Текстовый файл |
| Lingua: | russo |
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2023
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| Accesso online: | http://earchive.tpu.ru/handle/11683/80906 |
| Natura: | Elettronico Capitolo di libro |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=674324 |
| Riassunto: | Заглавие с экрана This article discusses possible methods for solving the problem of classifying bitcoin transactions into licit and illicit ones. The proposed options for solving this problem of anti-money laundering analytics based on graph neural networks with different architecture. The results of computational experiments using data from the Elliptic Data Set are presented. Текстовый файл |
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