Both Homophily and Heterophily Matter: Bi-Path Aware Graph Neural Network for Ethereum Account Classification

【Author】 Yang, Han; Fang, Junyuan; Wu, Jiajing; Zheng, Zibin

【Source】IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS

【影响因子】5.877

【Abstract】In recent years, the cryptocurrency market has been booming with an ever-increasing market capitalization. However, due to the anonymity of blockchain technology, this market has become a hotbed of financial crimes. As the largest blockchain platform supporting smart contracts, financial crimes including scams and hacking frequently happen on Ethereum and have caused serious losses. Therefore, it is necessary to classify Ethereum accounts in order to better identify those involved in illegal transactions and analyze the behavior patterns of different classes of accounts. In this paper, we construct an Ethereum transaction network based on transaction records and find that this network is with heterophily. However, most of the current work on account classification ignores the role of this heterophily information. We first figure out that the heterophily information of the neighborhood may also be beneficial for the final predictions. Based on this, we propose a new graph neural network (GNN) model, named BPA-GNN, which incorporates both homophilic and heterophilic information into the neighborhood aggregations. Specifically, BPA-GNN consists of three main modules including bi-path neighbor sampling, separated neighborhood aggregation, and attention-based node representation learning. Comprehensive experiments on a real Ethereum transaction dataset demonstrate the state-of-the-art performance of BPA-GNN, showing that the model can effectively extract and utilize neighborhood information to improve the distinguishability of node representations. As an effective solution for Ethereum account de-anonymization, BPA-GNN can help identify illegal activities and promote the healthy development of the Ethereum ecosystem.

【Keywords】Ethereum; de-anonymization; graph neural network; heterophily

【发表时间】2023 SEP

【收录时间】2024-03-12

【文献类型】实验仿真

【主题类别】

区块链治理-技术治理-实体分类