PR-Graph2vec: PageRank-based Graph Embedding Algorithm for Phishing Detection in the Ethereum Network

Abstract

Ethereum, as one of today's most active blockchain platforms, provides extensive data for academic interest, thanks to its transparency and has garnered broad academic interest. Many studies model Ethereum transaction records as graph structures and design models to analyze phishing address transaction features. However, they often neglect the deep network structure of transaction sub-calls and lack efficient random walk strategies for optimal subgraph sampling. Therefore, this study introduces PR-Graph2vec, a PageRank- based graph embedding algorithm that captures the topological features of network phishing attacks from both the contract and sub-call perspectives, thereby improving phishing address detection. We extracted all transactions labeled as phishing from the blockchain explorer EtherScan to build a transaction network graph. Then, by aggregating the features of transaction call types and account types, we performed in-depth feature mining on transaction sub-calls. Finally, we used PageRank for biased sampling, significantly reducing the graph size. Experimental results demonstrate that our proposed Ethereum phishing detection model, PR-Graph2vec, excels in practical applications, achieving 91.0% precision and an 86.9% F1-score.