Author:
Wei Tianpeng,Zeng Biyang,Guo Wenqi,Guo Zhenyu,Tu Shikui,Xu Lei
Publisher
Springer Nature Singapore
Reference15 articles.
1. Alarab, I., Prakoonwit, S.: Graph-based LSTM for anti-money laundering: experimenting temporal graph convolutional network with bitcoin data. Neural Process. Lett. 55(1), 689–707 (2023)
2. Cui, Z., Li, Z., et al.: Dygcn: Efficient dynamic graph embedding with graph convolutional network. IEEE Trans. Neural Netw. Learning Syst. (2022)
3. Feng, Y., Li, C., et al.: Anti-money laundering (AML) research: a system for identification and multi-classification. In: Ni, W., Wang, X., Song, W., Li, Y. (eds.) Web Information Systems and Applications, pp. 169–175. Springer International Publishing, Cham (2019)
4. Gori, M., Monfardini, G., Scarselli, F.: A new model for learning in graph domains. In: Proceedings of the 2005 IEEE International Joint Conference on Neural Networks, vol. 2, pp. 729–734. IEEE (2005)
5. Jullum, M., Løland, A., et al.: Detecting money laundering transactions with machine learning. J. Money Laundering Control 23(1), 173–186 (2020)
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