SAMamba: Structure-Aware Mamba for Ethereum Fraud Detection

The pseudonymity nature of Ethereum provides a protective umbrella for criminal activities, allowing criminals to develop a series of black industries such as phishing scams in unregulated areas. In order to exploit the relational inductive bias to discover the real identity of anonymous accounts, graph neural networks (GNNs) have been widely used in Ethereum fraud detection tasks as an effective and powerful framework. However, the expressive power of GNN’s 1-hop message passing mechanism is bounded by the Weisfeiler-Leman (1-WL) test, degrading the fraud detection performance on the Ethereum network. This paper proposes a structure-aware Mamba framework, named SAMamba. Specifically, SAMamba uses a subgraph encoding strategy to capture complex structural patterns and introduces Mamba’s exceptional sequence modeling capabilities to route global information. In order to filter task-relevant information from dense information, the attention mechanism and the selection mechanism are introduced from local and global perspectives, respectively. These tailor-made designs enable SAMamba to distinguish subtle differences in structural patterns and selectively aggregate task-oriented information, thereby demonstrating exceptional performance in fraud detection tasks. Extensive experiments on real-world Ethereum data demonstrate that SAMamba outperforms state-of-the-art methods. The codes are publicly available on Github: https://github.com/deepang-ai/SAMamba