# Time-aware Metapath Feature Augmentation for Ponzi Detection in Ethereum

arXiv: https://arxiv.org/abs/2210.16863 · HTML: https://mlsanity.com/paper/2210.16863/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 56 references that are in the map.
- First posted on arXiv 30 Oct 2022.

## Builds on (most cited first, 24 of 24)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [Graph Transformer Networks](https://mlsanity.com/paper/1911.06455/) (2019)
- [Heterogeneous Information Network Embedding for Recommendation](https://mlsanity.com/paper/1711.10730/) (2017)
- [A Survey of Heterogeneous Information Network Analysis](https://mlsanity.com/paper/1511.04854/) (2015)
- [A Comprehensive Survey on Community Detection with Deep Learning](https://mlsanity.com/paper/2105.12584/) (2021)
- [Who Are the Phishers? Phishing Scam Detection on Ethereum via Network Embedding](https://mlsanity.com/paper/1911.09259/) (2019)
- [Dissecting Ponzi schemes on Ethereum: identification, analysis, and impact](https://mlsanity.com/paper/1703.03779/) (2017)
- [Behavior-aware Account De-anonymization on Ethereum Interaction Graph](https://mlsanity.com/paper/2203.09360/) (2022)
- [Subgraph Networks with Application to Structural Feature Space Expansion](https://mlsanity.com/paper/1903.09022/) (2019)
- [A Survey on the Security of Blockchain Systems](https://mlsanity.com/paper/1802.06993/) (2018)
- [M-Evolve: Structural-Mapping-Based Data Augmentation for Graph Classification](https://mlsanity.com/paper/2007.05700/) (2020)
- [Embedding Heterogeneous Networks into Hyperbolic Space Without Meta-path](https://mlsanity.com/paper/2106.09923/) (2021)
- [Heterogeneous Feature Augmentation for Ponzi Detection in Ethereum](https://mlsanity.com/paper/2204.08916/) (2022)
- [Target Defense Against Link-Prediction-Based Attacks via Evolutionary Perturbations](https://mlsanity.com/paper/1809.05912/) (2018)
- [RobustECD: Enhancement of Network Structure for Robust Community Detection](https://mlsanity.com/paper/1911.01670/) (2019)
- [Dual-channel Early Warning Framework for Ethereum Ponzi Schemes](https://mlsanity.com/paper/2206.07895/) (2022)
- [Forsage: Anatomy of a Smart-Contract Pyramid Scheme](https://mlsanity.com/paper/2105.04380/) (2021)
- [Ponzi Scheme Detection in EthereumTransaction Network](https://mlsanity.com/paper/2104.08456/) (2021)

## Built on by (most cited first, 4 of 4)

- [Clarify Confused Nodes via Separated Learning](https://mlsanity.com/paper/2306.02285/) (2023)
- [Machine Learning for Blockchain Data Analysis: Progress and Opportunities](https://mlsanity.com/paper/2404.18251/) (2024)
- [Enhancing Ethereum Fraud Detection via Generative and Contrastive Self-supervision](https://mlsanity.com/paper/2408.00641/) (2024)
- [Unveiling Latent Information in Transaction Hashes: Hypergraph Learning for Ethereum Ponzi Scheme Detection](https://mlsanity.com/paper/2503.21463/) (2025)

## Abstract (arXiv)

With the development of Web 3.0 which emphasizes decentralization, blockchain technology ushers in its revolution and also brings numerous challenges, particularly in the field of cryptocurrency. Recently, a large number of criminal behaviors continuously emerge on blockchain, such as Ponzi schemes and phishing scams, which severely endanger decentralized finance. Existing graph-based abnormal behavior detection methods on blockchain usually focus on constructing homogeneous transaction graphs without distinguishing the heterogeneity of nodes and edges, resulting in partial loss of transaction pattern information. Although existing heterogeneous modeling methods can depict richer information through metapaths, the extracted metapaths generally neglect temporal dependencies between entities and do not reflect real behavior. In this paper, we introduce Time-aware Metapath Feature Augmentation (TMFAug) as a plug-and-play module to capture the real metapath-based transaction patterns during Ponzi scheme detection on Ethereum. The proposed module can be adaptively combined with existing graph-based Ponzi detection methods. Extensive experimental results show that our TMFAug can help existing Ponzi detection methods achieve significant performance improvements on the Ethereum dataset, indicating the effectiveness of heterogeneous temporal information for Ponzi scheme detection.