Minimal Substitution-based Label Propagation for Anomalous Blockchain Detection

Due to the lack of centralized regulatory authorities, the cryptocurrency trading market has witnessed an increase in illicit activities. Recently, more and more researchers have started exploring the application of machine learning techniques to achieve anomaly detection in these transaction networks. However, in this transaction network, it’s hard to find the abnormal nodes because the labeling cost is high, without enough labeled and imbalanced data, which greatly limits the performance of the detection model. Therefore, this paper proposes a Minimal Substitution-based Label Propagation (MSLP) model to provide more labeled data to balance the graph data and complement the sample for downstream anomalous transaction detection in the blockchain networks. Specifically, MSLP is the first method that utilizes the minimal substitution theory from the social computing field to find more minority nodes from the unlabeled nodes. This approach has the potential to obtain more high-quality labeled data with minimal computational cost by utilizing a small amount of labeled graph data. Then, a label evaluation mechanism is proposed to decide the number of samples to be adopted for each class, ensuring the performance of downstream graph neural networks. Finally, extensive experiments were conducted and the proposed model improve the F1 score of illegal transaction node identification by 2.6% to 8.2%.