# TTAGN: Temporal Transaction Aggregation Graph Network for Ethereum Phishing Scams Detection

arXiv: https://arxiv.org/abs/2204.13442 · HTML: https://mlsanity.com/paper/2204.13442/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 33 references that are in the map.
- First posted on arXiv 28 Apr 2022.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [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)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [Graph Embedding Techniques, Applications, and Performance: A Survey](https://mlsanity.com/paper/1705.02801/) (2017)
- [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)
- [Modeling and Understanding Ethereum Transaction Records via a Complex Network Approach](https://mlsanity.com/paper/2012.15462/) (2020)

## Built on by (most cited first, 18 of 18)

- [BERT4ETH: A Pre-trained Transformer for Ethereum Fraud Detection](https://mlsanity.com/paper/2303.18138/) (2023)
- [Demystifying Fraudulent Transactions and Illicit Nodes in the Bitcoin Network for Financial Forensics](https://mlsanity.com/paper/2306.06108/) (2023)
- [Ethereum Fraud Detection via Joint Transaction Language Model and Graph Representation Learning](https://mlsanity.com/paper/2409.07494/) (2024)
- [Benchmarking Graph Neural Networks for Document Layout Analysis in Public Affairs](https://mlsanity.com/paper/2505.14699/) (2025)
- [Evolve Path Tracer: Early Detection of Malicious Addresses in Cryptocurrency](https://mlsanity.com/paper/2301.05412/) (2023)
- [GAT-COBO: Cost-Sensitive Graph Neural Network for Telecom Fraud Detection](https://mlsanity.com/paper/2303.17334/) (2023)
- [EX-Graph: A Pioneering Dataset Bridging Ethereum and X](https://mlsanity.com/paper/2310.01015/) (2023)
- [LMAE4Eth: Generalizable and Robust Ethereum Fraud Detection by Exploring Transaction Semantics and Masked Graph Embedding](https://mlsanity.com/paper/2509.03939/) (2025)
- [PulseReddit: A Novel Reddit Dataset for Benchmarking MAS in High-Frequency Cryptocurrency Trading](https://mlsanity.com/paper/2506.03861/) (2025)
- [From Asset Flow to Status, Action and Intention Discovery: Early Malice Detection in Cryptocurrency](https://mlsanity.com/paper/2309.15133/) (2023)
- [Machine Learning on Blockchain Data: A Systematic Mapping Study](https://mlsanity.com/paper/2403.17081/) (2024)
- [Investigating Similarities Across Decentralized Financial (DeFi) Services](https://mlsanity.com/paper/2404.00034/) (2024)
- [Enhancing Ethereum Fraud Detection via Generative and Contrastive Self-supervision](https://mlsanity.com/paper/2408.00641/) (2024)
- [Optimizing Blockchain Analysis: Tackling Temporality and Scalability with an Incremental Approach with Metropolis-Hastings Random Walks](https://mlsanity.com/paper/2501.12491/) (2025)
- [Probabilistic Sampling-Enhanced Temporal-Spatial GCN: A Scalable Framework for Transaction Anomaly Detection in Ethereum Networks](https://mlsanity.com/paper/2310.00144/) (2023)
- [Linking Cryptoasset Attribution Tags to Knowledge Graph Entities: An LLM-based Approach](https://mlsanity.com/paper/2502.10453/) (2025)
- [Fishing for Phishers: Learning-Based Phishing Detection in Ethereum Transactions](https://mlsanity.com/paper/2504.17953/) (2025)
- [Population-Calibrated Graph Screening at 835-Million-Address Scale, with Label-Free Transfer to New Chains](https://mlsanity.com/paper/2609.03036/) (2026)

## Abstract (arXiv)

In recent years, phishing scams have become the most serious type of crime involved in Ethereum, the second-largest blockchain platform. The existing phishing scams detection technology on Ethereum mostly uses traditional machine learning or network representation learning to mine the key information from the transaction network to identify phishing addresses. However, these methods adopt the last transaction record or even completely ignore these records, and only manual-designed features are taken for the node representation. In this paper, we propose a Temporal Transaction Aggregation Graph Network (TTAGN) to enhance phishing scams detection performance on Ethereum. Specifically, in the temporal edges representation module, we model the temporal relationship of historical transaction records between nodes to construct the edge representation of the Ethereum transaction network. Moreover, the edge representations around the node are aggregated to fuse topological interactive relationships into its representation, also named as trading features, in the edge2node module. We further combine trading features with common statistical and structural features obtained by graph neural networks to identify phishing addresses. Evaluated on real-world Ethereum phishing scams datasets, our TTAGN (92.8% AUC, and 81.6% F1score) outperforms the state-of-the-art methods, and the effectiveness of temporal edges representation and edge2node module is also demonstrated.