Heterophily-Aware Graph Learning for Ethereum Phishing Detection via Semantic Motif Disentanglement

Abstract

Graph Neural Networks (GNNs) have become the de facto standard for modeling blockchain transaction networks. However, standard GNN architectures predominantly operate under the assumption of homophily-that connected nodes share similar labels or features. This assumption catastrophically fails in the context of Ethereum phishing detection, a quintessential heterophilic learning problem where fraudsters (phishing accounts) actively connect with unlike victims (normal accounts) to facilitate theft. In this work, we argue that the "homophily bottleneck" in existing detection systems obscures critical high-frequency signals necessary for identifying illicit activity. We introduce the Heterophilic Semantic Graph Framework (HSGF), a novel architecture designed to decouple structural roles from feature smoothing. HSGF integrates a Motif-based Semantic Sampling (MSS) strategy to capture complex, directed transactional intents (e.g., dispersing, gathering, mixing) and a Heterophily-Aware Feature Fusion (HAFF) module that prevents the oversmoothing of fraudster representations into victim representations. Extensive experiments on real-world Ethereum datasets demonstrate that HSGF significantly outperforms state-of-the-art baselines, particularly in class-imbalanced scenarios, effectively breaking the ceiling imposed by traditional homophilic aggregation.