Summary of Ethereum Fraud Detection with Heterogeneous Graph Neural Networks, by Hiroki Kanezashi et al.
Ethereum Fraud Detection with Heterogeneous Graph Neural Networks
by Hiroki Kanezashi, Toyotaro Suzumura, Xin Liu, Takahiro Hirofuchi
First submitted to arxiv on: 23 Mar 2022
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a comparative study on graph neural network (GNN) models for detecting phishing transactions in Ethereum’s large-scale transaction networks. It highlights the importance of considering both node and edge heterogeneity in GNN models to improve their accuracy. The authors exhaustively compare and verify various GNN models, including homogeneous and heterogeneous ones, using actual Ethereum transaction data and reported label data. Their results show that heterogeneous models outperform homogeneous models, with RGCN achieving the best overall performance metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Phishing attacks in cryptocurrency transactions are a growing concern. Researchers have been working on developing machine learning models to detect these suspicious activities. This paper compares different types of graph neural network (GNN) models for detecting phishing in Ethereum’s large-scale transaction networks. The authors find that some GNN models work better than others, depending on the type of data they use and how they’re trained. They hope their results will help improve the accuracy of these detection systems. |
Keywords
* Artificial intelligence * Gnn * Graph neural network * Machine learning
