概念漂移下比特币反洗钱的拓扑韧性：一种解耦双路径图神经网络

In recent years, Graph Neural Networks have shown some applications in the area of Bitcoin Anti-Money Laundering Detection. However, the actual performance of Graph Neural Networks (GNNs) in applications has often been significantly overestimated in previous research. Most existing studies use random segmentations of the data and pre-calculated global statistical features, causing concept drift. In order to facilitate effective detection in real-world forensic scenarios, this paper proposes a Topological Synchronization Graph Network (TSG-Net). A disentangled two-path architecture has been used. On one hand, it uses a multi-head graph-attention module to identify dynamic Topology-Aware Anomalies. On the other hand, it maintains the original attribute characteristics of the transaction node using a residual module. The findings indicate that when confronted with evolving transactions in subsequent time steps, the efficacy of conventional static models is significantly diminished, while TSG-Net exhibits superior temporal resilience. It was demonstrated that, while maintaining an F1 score above 0.75, the model increased the precision for illegal nodes to 96%. Based on the visualization of the hidden-layer manifold, this paper also verifies that end-to-end graph representation learning is effective for resolving complex financial obfuscation problems.