DyHDGE: Dynamic heterogeneous transaction graph embedding for safety-centric fraud detection in financial scenarios
Dynamic graph fraud detection aims to distinguish fraudulent entities that deviate significantly from most benign entities within an ever-changing graph network. However, when dealing with different financial fraud scenarios, existing methods face challenges, resulting in difficulty in effectively e...
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| Hlavní autoři: | , , , |
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| Médium: | Artigo |
| Jazyk: | Inglês |
| Vydáno: |
KeAi Communications Co., Ltd.
2024-12-01
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| Edice: | Journal of Safety Science and Resilience |
| Témata: | |
| On-line přístup: | http://www.sciencedirect.com/science/article/pii/S2666449624000483 |
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