HNN4RP: Heterogeneous Graph Neural Network for Rug Pull Detection on Ethereum

LI Chenchen; JIN Hai; WU Minrui; XIAO Jiang

doi:10.11999/JEIT250160

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LI Chenchen, JIN Hai, WU Minrui, XIAO Jiang. HNN4RP: Heterogeneous Graph Neural Network for Rug Pull Detection on Ethereum[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250160

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LI Chenchen, JIN Hai, WU Minrui, XIAO Jiang. HNN4RP: Heterogeneous Graph Neural Network for Rug Pull Detection on Ethereum[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250160

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HNN4RP: Heterogeneous Graph Neural Network for Rug Pull Detection on Ethereum

doi: 10.11999/JEIT250160 cstr: 32379.14.JEIT250160

1.
National Engineering Research Center for Big Data Technology and System, Wuhan 430074, China
2.
Services Computing Technology and System Laboratory, Wuhan 430074, China
3.
Cluster and Grid Computing Laboratory, Wuhan 430074, China
4.
School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China

Funds: The National Natural Science Foundation of China (62472185)

Received Date: 2025-03-13
Rev Recd Date: 2025-07-28

Available Online: 2025-08-05

Abstract

Abstract

Objective The rapid expansion of Decentralized Finance (DeFi) has been accompanied by a rise in fraudulent activities, with Rug Pull scams posing a significant threat to the security, stability, and credibility of blockchain ecosystems. Due to the permissionless and decentralized nature of DeFi platforms, malicious actors can exploit smart contracts to deceive investors, artificially inflate token prices, and withdraw liquidity abruptly, leaving investors with worthless assets. The anonymous and pseudonymous characteristics of blockchain transactions further hinder fraud detection and regulatory oversight, making Rug Pull scams one of the most challenging forms of financial fraud in the DeFi sector. Although traditional fraud detection approaches, such as rule-based heuristics and statistical analysis, provide basic risk assessment, they often fail to capture the complex transaction patterns, dynamic interactions, and heterogeneous relationships within blockchain networks. To address these limitations, this study proposes Heterogeneous graph Neural Network for Rug Pull detection (HNN4RP), a fraud detection framework based on Heterogeneous Graph Neural Networks (GNNs), specifically designed for the early identification of Rug Pull scams. By leveraging the graph structure of blockchain transactions, HNN4RP models Ethereum transaction data as a heterogeneous graph to capture the relationships among smart contracts, liquidity pools, and trader addresses. The framework integrates advanced representation learning techniques to reveal hidden dependencies, detect anomalous patterns, and differentiate between legitimate and fraudulent behaviors in a scalable, data-driven manner. Furthermore, HNN4RP incorporates temporal dynamics and contextual information to adapt to evolving fraud tactics. Methods HNN4RP models Ethereum transactions as a heterogeneous graph, distinguishing between regular nodes and smart contract nodes while preserving both structured and unstructured transactional data. By leveraging a graph-based representation, the model captures complex interactions within the Ethereum network, supporting comprehensive analysis of transactional behavior. This approach explicitly represents key relationships between entities, including token transfers, contract interactions, and liquidity movements, thereby facilitating more accurate fraud detection.The core components of HNN4RP include a Random Walk with Restart (RWR) sampling mechanism, which mitigates biases caused by skewed transaction distributions and enables balanced exploration of node neighborhoods. This technique captures long-range dependencies in the transaction network while preserving the local structural context, which is essential for detecting anomalous behaviors associated with fraud. By dynamically adjusting the probability of revisiting specific nodes, RWR improves the model’s focus on relevant transactional pathways, reducing the effects of data sparsity and imbalance.In addition to RWR-based sampling, HNN4RP incorporates multi-modal feature encoding to capture both structured transactional relationships and unstructured node attributes, such as smart contract code and token metadata. This integration enables the model to leverage a diverse set of informative features, improving its ability to differentiate between legitimate and fraudulent activities. By encoding both numerical and textual representations of transaction data, the framework enhances the robustness of fraud detection against evasive tactics employed by malicious actors.Furthermore, HNN4RP employs a BiLSTM-based temporal fusion mechanism to retain the sequential dependencies within transaction histories, effectively modeling temporal behavioral patterns. This component is essential for capturing the evolution of fraudulent activities over time, as Rug Pull scams often exhibit time-dependent characteristics. By learning the progression of transactional patterns, the model enhances fraud classification accuracy and enables early detection of fraudulent schemes before they escalate. Through this comprehensive approach, HNN4RP provides a scalable, adaptive, and high-precision solution for fraud detection in decentralized financial ecosystems. Results and Discussions The performance of the proposed HNN4RP model is evaluated against three categories of baseline methods: attribute-based approaches, random walk-based algorithms, and deep learning models. Across all dataset scales, HNN4RP consistently outperforms these baselines across three predefined evaluation metrics, demonstrating superior fraud detection capability. Notably, on the D2 and D3 datasets, HNN4RP achieves an accuracy of 96% and an F1 score of 0.95 (Table 2). Ablation experiments indicate that both attribute-based features and textual representations contribute significantly to Rug Pull detection performance (Fig. 3). Parameter sensitivity analysis shows that the optimal neighborhood size is 16, and the best embedding dimension is 2⁷ (Figs. 4 and 5). Further experiments incorporating different types of noise interference confirm that HNN4RP exhibits strong robustness to data perturbations, with the F1 score declining by no more than 7% under moderate noise levels (Table 3). Early fraud detection tests demonstrate that HNN4RP effectively identifies fraudulent activities at an early stage, providing reliable warning signals before scams fully develop (Table 4). Conclusions This study addresses the emerging challenge of Rug Pull scams in the DeFi and Web3 domains and proposes a deep learning-based detection model, HNN4RP, with significant implications for the stability and security of decentralized financial ecosystems. By formalizing the Rug Pull detection task using heterogeneous graph modeling, this work tackles three major challenges in Ethereum-based fraud detection: biased transaction exploration, incomplete feature representation, and insufficient temporal pattern recognition. These challenges are addressed through the integration of RWR-based random walks, heterogeneous node feature extraction, and BiLSTM-based temporal feature fusion. Experimental results on real Ethereum transaction datasets demonstrate that HNN4RP achieves 96% accuracy and provides reliable early warnings within four hours before fraudulent activities occur, confirming its practical applicability for early fraud detection in decentralized environments.
- Blockchain,
- Ethereum,
- Decentralized Finance (DeFi),
- Graph Neural Network(GNN),
- Rug pull

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References(45)

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