# Heterogeneous Feature Augmentation for Ponzi Detection in Ethereum

arXiv: https://arxiv.org/abs/2204.08916 · HTML: https://mlsanity.com/paper/2204.08916/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2022.

## Built on by (most cited first, 9 of 9)

- [Continual Learning on Graphs: A Survey](https://mlsanity.com/paper/2402.06330/) (2024)
- [Network Analytics for Anti-Money Laundering -- A Systematic Literature Review and Experimental Evaluation](https://mlsanity.com/paper/2405.19383/) (2024)
- [Time-aware Metapath Feature Augmentation for Ponzi Detection in Ethereum](https://mlsanity.com/paper/2210.16863/) (2022)
- [Graph Anomaly Detection at Group Level: A Topology Pattern Enhanced Unsupervised Approach](https://mlsanity.com/paper/2308.01063/) (2023)
- [EG-ConMix: An Intrusion Detection Method based on Graph Contrastive Learning](https://mlsanity.com/paper/2403.17980/) (2024)
- [Enhancing Ethereum Fraud Detection via Generative and Contrastive Self-supervision](https://mlsanity.com/paper/2408.00641/) (2024)
- [Advances in Continual Graph Learning for Anti-Money Laundering Systems: A Comprehensive Review](https://mlsanity.com/paper/2503.24259/) (2025)
- [Unveiling Latent Information in Transaction Hashes: Hypergraph Learning for Ethereum Ponzi Scheme Detection](https://mlsanity.com/paper/2503.21463/) (2025)
- [CrossHGL: A Text-Free Foundation Model for Cross-Domain Heterogeneous Graph Learning](https://mlsanity.com/paper/2603.27685/) (2026)

## Abstract (arXiv)

While blockchain technology triggers new industrial and technological revolutions, it also brings new challenges. Recently, a large number of new scams with a "blockchain" sock-puppet continue to emerge, such as Ponzi schemes, money laundering, etc., seriously threatening financial security. Existing fraud detection methods in blockchain mainly concentrate on manual feature and graph analytics, which first construct a homogeneous transaction graph using partial blockchain data and then use graph analytics to detect anomaly, resulting in a loss of pattern information. In this paper, we mainly focus on Ponzi scheme detection and propose HFAug, a generic Heterogeneous Feature Augmentation module that can capture the heterogeneous information associated with account behavior patterns and can be combined with existing Ponzi detection methods. HFAug learns the metapath-based behavior characteristics in an auxiliary heterogeneous interaction graph, and aggregates the heterogeneous features to corresponding account nodes in the homogeneous one where the Ponzi detection methods are performed. Comprehensive experimental results demonstrate that our HFAug can help existing Ponzi detection methods achieve significant performance improvement on Ethereum datasets, suggesting the effectiveness of heterogeneous information on detecting Ponzi schemes.