TFD-GCL: Telecommunications Fraud Detection Based on Graph Contrastive Learning with Adaptive Augmentation

Telecommunications fraud incidents have occurred frequently around the world. Rapid and accurate detection of fraudsters in telecommunication networks has become a hot topic for researchers. Due to data imbalance and dependency on labels, many Graph Neural Networks (GNNs)-based methods cannot achieve good representation ability in Telecommunications Fraud Detection (Telecom-FD) tasks. Recently, Graph Contrast Learning (GCL) has emerged as a successful method for fraud detection due to its unsupervised graph representation learning ability. However, existing GCL-based methods typically adopt uniform strategies to drop edges and impose perturbations on features during the data augmentation process, which tends to eliminate certain influential edges, consequently reducing the quality of the embeddings. To overcome the above problems, a novel framework named TFD-GCL is proposed to adaptively generate contrastive instances for effective Telecom-FD. Specifically, encoding anchor nodes and generating positive instances by automatically learning from neighbors using the attention mechanism. Then, the end-to-end adaptive learning global average similarity is devised to evaluate interaction information between neighbors and construct negative instances from dissimilar neighbors. In addition, TFD-GCL uses a joint contrastive loss to improve the representational capability of fraudulent nodes in unbalanced data. Extensive experiments on the real-world dataset demonstrate that TFD-GCL has better performance compared with classical Telecom-FD methods when the labels are extremely limited.