Similarity-Guided Graph Learning with Embedding-Space Oversampling for Anomaly Detection in Heterophilic Cryptocurrency Transaction Networks

Abstract

The rapid development of cryptocurrency has led to an urgent demand for anomaly detection targeting illegal transaction activities. Cryptocurrency transaction networks have two core characteristics: class imbalance and connection heterophily. Existing anomaly-detection methods tend to cause over-smoothing of anomaly nodes’ features and have low recall rates for anomaly nodes, resulting in poor detection performance. Although some studies have noticed these challenges separately, there is still a lack of a unified solution framework. This paper proposes a cryptocurrency anomaly-detection framework specially designed for cryptocurrency transaction scenarios. It consists of three core modules: Heterophily-adaptive Aggregation Module, Generation-based Minority Class Oversampling Module, and Ensemble Anomaly-Detection Classifier. The Heterophily-adaptive Aggregation Module enables the framework to resist heterophily noise during learning node embeddings by adaptively adjusting the aggregation weights of neighboring nodes. The Generation-based Minority Class Oversampling Module augments the learned minority node embeddings in the embedding space to obtain a more balanced class distribution. Finally, the Ensemble Anomaly-Detection Classifier is used to predict the detection result. Experimental results on multiple cryptocurrency transaction datasets show that the proposed framework significantly outperforms traditional machine learning methods and mainstream graph neural network models.