Voucher abuse detection with prompt-based fine-tuning on graph neural networks

• Main Authors: WEN, Zhihao, FANG, Yuan, LIU, Yihan, GUO, Yang, HAO, Shuji
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: anomaly detection; graph neural networks; pre-training; prompt; Artificial Intelligence and Robotics
• Online Access: https://ink.library.smu.edu.sg/sis_research/8251; https://ink.library.smu.edu.sg/context/sis_research/article/9254/viewcontent/CIKM23_VPGNN.pdf
• Institution: Singapore Management University

Voucher abuse detection is an important anomaly detection problem in E-commerce. While many GNN-based solutions have emerged, the supervised paradigm depends on a large quantity of labeled data. A popular alternative is to adopt self-supervised pre-training using label-free data, and further fine-tune on a downstream task with limited labels. Nevertheless, the "pre-train, fine-tune" paradigm is often plagued by the objective gap between pre-training and downstream tasks. Hence, we propose VPGNN, a prompt-based fine-tuning framework on GNNs for voucher abuse detection. We design a novel graph prompting function to reformulate the downstream task into a similar template as the pretext task in pre-training, thereby narrowing the objective gap. Extensive experiments on both proprietary and public datasets demonstrate the strength of VPGNN in both few-shot and semi-supervised scenarios. Moreover, an online deployment of VPGNN in a production environment shows a 23.4% improvement over two existing deployed models.