Burstiness-aware Bipartite Graph Neural Networks for Fraudulent User Detection on Rating Platforms

As the digital commerce landscape continues to expand with rating platforms, the consumer base has similarly grown, marking a pivotal reliance on user ratings and reviews. However, the rise of fraudulent users leveraging deceitful conduct, such as manipulation of product rankings, challenges the credibility of these platforms and compels the necessity for an effective detection model. Amid the challenges of evolving fraudulent patterns and label scarcity, this paper presents a novel model, Burstiness-aware Bipartite Graph Neural Networks (BurstBGN), which combats fraud by exploiting user-product bipartite graphs and timestamped rating activities. BurstBGN encapsulates two key ideas: the modeling of user-product interaction via historical rating data through an Edge-time GNN module and the exhaustive mapping of bursty fraudulent user activities. The performance of BurstBGN is demonstrated through rigorous benchmarking against established methods across three datasets. Our results show that BurstBGN consistently outperforms these methods under both transductive and inductive settings, confirming its effectiveness in detecting fraudulent users from limited annotated data, and thereby providing a safeguard for maintaining user trust in e-commerce platforms.