When LLM Meets Simplicial Complex: A Novel Graph Prompt Learning on Ethereum Transaction Networks

Abstract

Fraudulent activity on blockchain networks poses significant risks to the integrity and trust of decentralized finance ecosystems. The timely and accurate detection of fraud nodes such as phishing addresses within large-scale Ethereum transaction networks remains a major challenge due to their dynamic, sparse, and evolving structures. While methods like graph deep learning (e.g., graph neural networks) have been extensively explored, they are not inherently designed to capture higherorder interactions and textual information embedded within graph data. Motivated by the urgent need for advanced and robust fraud detection techniques, we introduce a novel graph prompting method named Large Language Model-Simplicial Complex (LLM-SC) based graph prompting framework that leverages LLM-based multi-agent collaboration system, LLMbased financial news prompt function, and simplicial neural networks to capture both the structural and contextual dimensions of blockchain activity. The empirical studies demonstrate the effectiveness of our approach, and these results provide a new tool for blockchain analytics platforms and regulatory authorities, enabling earlier and more accurate identification of fraudulent behavior and ultimately supporting safer and more resilient digital financial systems. The code is available at https://github.com/y13564/LLM-SC.