RAMA: Resistance-Aware Multi-Hop Aggregation Graph Representation Learning for Robust Ethereum Account Classification

Fraudulent behaviors in Ethereum, such as Ponzi schemes and phishing attacks, pose persistent threats to blockchain security. Classifying high-risk accounts is crucial for mitigating these threats. Given the graph-structured nature of Ethereum transactions, Graph Neural Networks (GNNs) are widely adopted for this task. However, conventional GNNs rely on local aggregation and treat all edges equally, which amplifies structural noise and limits the ability to capture weak but semantically meaningful connections. This limitation arises from the inability to distinguish informative links from spurious ones during message passing, especially in noisy and long-range transaction graphs. To address this, we propose RAMA, a Resistance-Aware Multi-hop Aggregation framework that reweights edges using resistance distance to reduce the influence of noisy links while preserving important topological information. It also employs attention-based multi-hop aggregation to effectively integrate both local and global transaction patterns. Experiments on real-world Ethereum datasets show that RAMA consistently outperforms strong baselines in Macro-F1 and remains robust under edge perturbations, demonstrating its effectiveness for practical account classification in blockchain environments.