# RiskSEA : A Scalable Graph Embedding for Detecting On-chain Fraudulent Activities on the Ethereum Blockchain

arXiv: https://arxiv.org/abs/2410.02160 · HTML: https://mlsanity.com/paper/2410.02160/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 28 references that are in the map.
- First posted on arXiv 3 Oct 2024.

## Builds on (most cited first, 11 of 11)

- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Dynamic Word Embeddings](https://mlsanity.com/paper/1702.08359/) (2017)
- [Who Are the Phishers? Phishing Scam Detection on Ethereum via Network Embedding](https://mlsanity.com/paper/1911.09259/) (2019)
- [dynnode2vec: Scalable Dynamic Network Embedding](https://mlsanity.com/paper/1812.02356/) (2018)
- [A Survey on Ethereum Systems Security: Vulnerabilities, Attacks and Defenses](https://mlsanity.com/paper/1908.04507/) (2019)
- [T-EDGE: Temporal WEighted MultiDiGraph Embedding for Ethereum Transaction Network Analysis](https://mlsanity.com/paper/1905.08038/) (2019)
- [Ethereum Fraud Detection with Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2203.12363/) (2022)
- [TSGN: Transaction Subgraph Networks for Identifying Ethereum Phishing Accounts](https://mlsanity.com/paper/2104.08767/) (2021)
- [Temporal-Amount Snapshot MultiGraph for Ethereum Transaction Tracking](https://mlsanity.com/paper/2102.08013/) (2021)
- [Phishing Fraud Detection on Ethereum using Graph Neural Network](https://mlsanity.com/paper/2204.08194/) (2022)
- [Towards Understanding Crypto Money Laundering in Web3 Through the Lenses of Ethereum Heists](https://mlsanity.com/paper/2305.14748/) (2023)

## Abstract (arXiv)

Like any other useful technology, cryptocurrencies are sometimes used for criminal activities. While transactions are recorded on the blockchain, there exists a need for a more rapid and scalable method to detect addresses associated with fraudulent activities. We present RiskSEA, a scalable risk scoring system capable of effectively handling the dynamic nature of large-scale blockchain transaction graphs. The risk scoring system, which we implement for Ethereum, consists of 1. a scalable approach to generating node2vec embedding for entire set of addresses to capture the graph topology 2. transaction-based features to capture the transactional behavioral pattern of an address 3. a classifier model to generate risk score for addresses that combines the node2vec embedding and behavioral features. Efficiently generating node2vec embedding for large scale and dynamically evolving blockchain transaction graphs is challenging, we present two novel approaches for generating node2vec embeddings and effectively scaling it to the entire set of blockchain addresses: 1. node2vec embedding propagation and 2. dynamic node2vec embedding. We present a comprehensive analysis of the proposed approaches. Our experiments show that combining both behavioral and node2vec features boosts the classification performance significantly, and that the dynamic node2vec embeddings perform better than the node2vec propagated embeddings.