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- W4386323676 abstract "In this paper, we propose a Graph Neural Network-based model to detect fraudulent users on e-commerce platforms without relying on rating scores. Utilizing user-product bipartite graphs and timestamp data, we capture temporal patterns and neighborhood information, creating a graph with multidimensional edge vectors. Our model demonstrates competitive performance compared to state-of-the-art methods, effectively identifying fraudulent users under data-insufficient conditions and enhancing the overall reliability of online platforms." @default.
- W4386323676 created "2023-09-01" @default.
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- W4386323676 date "2023-07-17" @default.
- W4386323676 modified "2023-09-27" @default.
- W4386323676 title "Fraudulent User Detection with Time-enhanced Graph Neural Networks on E-Commerce Platforms" @default.
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- W4386323676 doi "https://doi.org/10.1109/icce-taiwan58799.2023.10226654" @default.
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