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

arXiv: https://arxiv.org/abs/2503.21463 · HTML: https://mlsanity.com/paper/2503.21463/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 27 Mar 2025.

## Builds on (most cited first, 10 of 10)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Hypergraph Neural Networks](https://mlsanity.com/paper/1809.09401/) (2018)
- [HNHN: Hypergraph Networks with Hyperedge Neurons](https://mlsanity.com/paper/2006.12278/) (2020)
- [Who Are the Phishers? Phishing Scam Detection on Ethereum via Network Embedding](https://mlsanity.com/paper/1911.09259/) (2019)
- [A Survey on Ethereum Systems Security: Vulnerabilities, Attacks and Defenses](https://mlsanity.com/paper/1908.04507/) (2019)
- [Heterogeneous Feature Augmentation for Ponzi Detection in Ethereum](https://mlsanity.com/paper/2204.08916/) (2022)
- [Dual-channel Early Warning Framework for Ethereum Ponzi Schemes](https://mlsanity.com/paper/2206.07895/) (2022)
- [Time-aware Metapath Feature Augmentation for Ponzi Detection in Ethereum](https://mlsanity.com/paper/2210.16863/) (2022)

## Abstract (arXiv)

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.