Fraudulent User Detection with Time-enhanced Graph Neural Networks on E-Commerce Platforms

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.