Unveiling Latent Information in Transaction Hashes: Hypergraph Learning for Ethereum Ponzi Scheme Detection

• URL: http://arxiv.org/abs/2503.21463v1
• Date: Thu, 27 Mar 2025 12:52:47 GMT
• Title: Unveiling Latent Information in Transaction Hashes: Hypergraph Learning for Ethereum Ponzi Scheme Detection
• Authors: Junhao Wu, Yixin Yang, Chengxiang Jin, Silu Mu, Xiaolei Qian, Jiajun Zhou, Shanqing Yu, Qi Xuan,
• Abstract summary: Existing fraud detection methods typically model transactions as graphs.<n>We propose a hypergraph modeling method for the Ponzi scheme detection method in which transactions are treated as hyperedges.<n>We show that the hyper-homo graph detection channel achieves significant performance improvements, demonstrating the superiority of hypergraph in Ponzi scheme detection.
• Score: 4.254242474314128
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the widespread adoption of Ethereum, financial frauds such as Ponzi schemes have become increasingly rampant in the blockchain ecosystem, posing significant threats to the security of account assets. Existing Ethereum fraud detection methods typically model account transactions as graphs, but this approach primarily focuses on binary transactional relationships between accounts, failing to adequately capture the complex multi-party interaction patterns inherent in Ethereum. To address this, we propose a hypergraph modeling method for the Ponzi scheme detection method in Ethereum, called HyperDet. Specifically, we treat transaction hashes as hyperedges that connect all the relevant accounts involved in a transaction. Additionally, we design a two-step hypergraph sampling strategy to significantly reduce computational complexity. Furthermore, we introduce a dual-channel detection module, including the hypergraph detection channel and the hyper-homo graph detection channel, to be compatible with existing detection methods. Experimental results show that, compared to traditional homogeneous graph-based methods, the hyper-homo graph detection channel achieves significant performance improvements, demonstrating the superiority of hypergraph in Ponzi scheme detection. This research offers innovations for modeling complex relationships in blockchain data.

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