Multilayer topology-aware graph contrastive learning for fraud detection in the Ethereum transaction network

Abstract

Fraud detection in blockchain networks presents unique challenges due to the decentralized and<br/>pseudonymous nature of transactions. This study introduces a novel Multilayer Topology-Aware Graph<br/>Contrastive Learning (MTGCL) framework to detect fraudulent activity within the Ethereum transaction<br/>network. The proposed approach leverages node-level and topology-level representations, integrating<br/>persistent homology to capture high-order structural patterns and enhance anomaly detection. By<br/>employing adaptive graph augmentation and self-supervised contrastive learning, MTGCL effectively<br/>improves fraud detection performance. Empirical evaluations demonstrate that MTGCL outperforms<br/>existing graph contrastive learning models in classification accuracy across multiple time periods while<br/>maintaining competitive computational efficiency. The framework also exhibits scalability for large-scale<br/>blockchain analysis, achieving lower computational costs compared with other baselines methods. These<br/>findings highlight MTGCL’s potential for real-world applications, offering valuable insights for financial<br/>institutions, cryptocurrency exchanges, regulatory bodies, and blockchain analytics firms in combating<br/>fraudulent activities and enhancing anti-money laundering compliance.