Contrastive Learning for Money Laundering Detection: Node-Subgraph-Node Method with Context Aggregation and Enhancement Strategy

Author:

Li Zhong,Huang Jialong,Yang Xueting,Qiu Meikang

Publisher

Springer Nature Singapore

Reference29 articles.

1. Elliptic analysis: New York husband and wife arrested for allegedly laundering \$5 billion in bitcoin stolen from Bitfinex in 2016. https://www.elliptic.co/blog/elliptic-analysis-new-york-husband-and-wife-arrested-for-laundering-5-billion-in-bitcoin-stolen-from-bitfinex-in-2016. Accessed 22 May 2024

2. Alarab, I., Prakoonwit, S., Nacer, M.I.: Comparative analysis using supervised learning methods for anti-money laundering in bitcoin. In: Proceedings of the 2020 5th International Conference on Machine Learning Technologies, Beijing, China, pp. 11–17 (2020)

3. Cardoso, M., Saleiro, P., Bizarro, P.: Laundrograph: Self-supervised graph representation learning for anti-money laundering. In: Proceedings of the 3th ACM International Conference on AI in Finance, New York, USA, pp. 130–138 (2022)

4. Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, Vienna, Austria, pp. 1597–1607 (2020)

5. Cheng, D., Ye, Y., Xiang, S., Ma, Z., Zhang, Y., Jiang, C.: Anti-money laundering by group-aware deep graph learning. IEEE Trans. Knowl. Data Eng. (2023). https://doi.org/10.1109/TKDE.2023.3272396

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