# Enhancing Ethereum Fraud Detection via Generative and Contrastive Self-supervision

arXiv: https://arxiv.org/abs/2408.00641 · HTML: https://mlsanity.com/paper/2408.00641/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 49 references that are in the map.
- First posted on arXiv 1 Aug 2024.

## Builds on (most cited first, 20 of 20)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Heterogeneous Graph Transformer](https://mlsanity.com/paper/2003.01332/) (2020)
- [Local Augmentation for Graph Neural Networks](https://mlsanity.com/paper/2109.03856/) (2021)
- [Interpretable and Efficient Heterogeneous Graph Convolutional Network](https://mlsanity.com/paper/2005.13183/) (2020)
- [Who Are the Phishers? Phishing Scam Detection on Ethereum via Network Embedding](https://mlsanity.com/paper/1911.09259/) (2019)
- [E-LSTM-D: A Deep Learning Framework for Dynamic Network Link Prediction](https://mlsanity.com/paper/1902.08329/) (2019)
- [An Overview on Smart Contracts: Challenges, Advances and Platforms](https://mlsanity.com/paper/1912.10370/) (2019)
- [Dissecting Ponzi schemes on Ethereum: identification, analysis, and impact](https://mlsanity.com/paper/1703.03779/) (2017)
- [BERT4ETH: A Pre-trained Transformer for Ethereum Fraud Detection](https://mlsanity.com/paper/2303.18138/) (2023)
- [TTAGN: Temporal Transaction Aggregation Graph Network for Ethereum Phishing Scams Detection](https://mlsanity.com/paper/2204.13442/) (2022)
- [Behavior-aware Account De-anonymization on Ethereum Interaction Graph](https://mlsanity.com/paper/2203.09360/) (2022)
- [Heterogeneous Feature Augmentation for Ponzi Detection in Ethereum](https://mlsanity.com/paper/2204.08916/) (2022)
- [Neighborhood Homophily-based Graph Convolutional Network](https://mlsanity.com/paper/2301.09851/) (2023)
- [Dual-channel Early Warning Framework for Ethereum Ponzi Schemes](https://mlsanity.com/paper/2206.07895/) (2022)
- [Clarify Confused Nodes via Separated Learning](https://mlsanity.com/paper/2306.02285/) (2023)
- [Time-aware Metapath Feature Augmentation for Ponzi Detection in Ethereum](https://mlsanity.com/paper/2210.16863/) (2022)
- [Ponzi Scheme Detection in EthereumTransaction Network](https://mlsanity.com/paper/2104.08456/) (2021)
- [Phishing Fraud Detection on Ethereum using Graph Neural Network](https://mlsanity.com/paper/2204.08194/) (2022)

## Built on by (most cited first, 1 of 1)

- [CrossHGL: A Text-Free Foundation Model for Cross-Domain Heterogeneous Graph Learning](https://mlsanity.com/paper/2603.27685/) (2026)

## Abstract (arXiv)

The rampant fraudulent activities on Ethereum hinder the healthy development of the blockchain ecosystem, necessitating the reinforcement of regulations. However, multiple imbalances involving account interaction frequencies and interaction types in the Ethereum transaction environment pose significant challenges to data mining-based fraud detection research. To address this, we first propose the concept of meta-interactions to refine interaction behaviors in Ethereum, and based on this, we present a dual self-supervision enhanced Ethereum fraud detection framework, named Meta-IFD. This framework initially introduces a generative self-supervision mechanism to augment the interaction features of accounts, followed by a contrastive self-supervision mechanism to differentiate various behavior patterns, and ultimately characterizes the behavioral representations of accounts and mines potential fraud risks through multi-view interaction feature learning. Extensive experiments on real Ethereum datasets demonstrate the effectiveness and superiority of our framework in detecting common Ethereum fraud behaviors such as Ponzi schemes and phishing scams. Additionally, the generative module can effectively alleviate the interaction distribution imbalance in Ethereum data, while the contrastive module significantly enhances the framework's ability to distinguish different behavior patterns. The source code will be available in https://github.com/GISec-Team/Meta-IFD.