The Elliptic2 dataset is a large-scale, labeled graph dataset containing 49M Bitcoin clusters and 196M transactions, with 2,763 subgraphs labeled as "suspicious" and 119,047 labeled as "licit". The dataset enables research on subgraph classification, a powerful technique for anti-money laundering (AML) in cryptocurrency.
The key insights from the paper are:
Subgraph representation learning can outperform traditional node-level graph neural network (GNN) approaches in identifying suspicious cryptocurrency transaction patterns. The GLASS model, which leverages the background graph structure, achieves high performance in classifying suspicious vs. licit subgraphs.
Validating the model predictions, the authors found that at least 26.9% of the accounts highlighted as suspicious by the model were confirmed to be involved in money laundering or fraud, compared to less than 0.1% of regular customer accounts. This demonstrates the practical value of the subgraph classification approach.
Further analysis of the suspicious subgraphs revealed known money laundering patterns, such as "peeling chains" and "nested services", providing additional confidence in the model's ability to uncover illicit activity.
Scaling subgraph learning to massive datasets like Elliptic2 requires efficient training systems that address the computational bottlenecks of neighborhood sampling and distributed feature storage. The authors discuss how systems like SALIENT and SALIENT++ can be adapted to handle subgraph classification workloads.
Overall, the Elliptic2 dataset and the insights from subgraph representation learning represent a significant advancement in applying AI techniques to combat financial crime in the cryptocurrency domain.
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by Claudio Bell... at arxiv.org 05-01-2024
https://arxiv.org/pdf/2404.19109.pdfDeeper Inquiries