DNLP-TCT: a dynamic network link prediction framework for tracking cryptocurrency transactions

In this paper, we investigate the challenge of cryptocurrency transaction tracking through dynamic network link prediction. Firstly, a transaction tracking model for Ethereum cryptocurrencies is proposed (DNLP-TCT), which employs a hybrid approach involving temporal attention mechanisms, Bi-directional Long Short-Term Memory Network (Bi-LSTM), and Autoencoder. Then, by leveraging the power of these machine learning techniques, the model implicitly captures the dynamic behavior and structure characteristic of the transaction network. This enables the prediction of future links, facilitating the inference of potential future transactions. Finally, the model is evaluated on three real Ethereum transaction datasets, and performance is compared against several representative methods. Based on the results, the proposed model exhibits significant improvements in both Area Under the ROC Curve (AUC) and Error Rate metrics, validating its effectiveness in the field of transaction tracking.