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.Comment: 7 pages, Accepted by CIKM23 Applied Research Trac
