Temporal Graph Representation Learning for Evolving User Behavior in Transactional Networks

This paper proposes a user risk classification model that combines dynamic graph neural networks with a self-supervised pretraining mechanism. The model is designed to address the dual challenges of dynamic user behavior and label scarcity in financial scenarios. First, a time-series graph is constructed to represent user transaction behaviors across different time slices. A dynamic graph convolution module is then applied to extract evolutionary features. Next, a node-level contrastive self-supervised task is designed. By constructing positive and negative node pairs, the model adaptively optimizes the representation space. This enhances the discriminative power of node embeddings during the unsupervised stage. Finally, the pretrained node representations are fed into a multi-layer perceptron for the downstream risk classification task. An empirical analysis is conducted on a publicly available credit card fraud detection dataset. The results show that the proposed model outperforms existing dynamic graph models in terms of accuracy, recall, and F1-score. It demonstrates a stronger ability in identifying high-risk user behavior, validating the effectiveness and robustness of the approach.