README.md

metadata

title: ETH Fraud Detection GraphSAGE
emoji: 🕵️‍♀️
colorFrom: indigo
colorTo: red
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: false
license: apache-2.0
tags:
  - blockchain
  - ethereum
  - graph-neural-networks
  - fraud-detection
  - security

Ethereum Fraud Detection System

This Space demonstrates a Graph Neural Network (GraphSAGE) model designed to detect illicit activities on the Ethereum blockchain.

🧠 Model Overview

The model operates on an inductive basis, meaning it learns to aggregate information from a node's local neighborhood (transactions in/out) to generate embeddings and predict the likelihood of fraud.

• Architecture: GraphSAGE (Graph Sample and Aggregate).
• Input Features: Transaction volume, degree (in/out), time-based features, and graph properties (PageRank, etc.).
• Output: A probability score (0-1) indicating the likelihood of the address being involved in criminal activity (Phishing, Hack, Scam).

📊 How to use

1. Enter an Ethereum address (must be present in the analyzed snapshot dataset).
2. The system looks up the pre-calculated risk score from the model inference.
3. It visualizes the Ego Graph (1-hop neighborhood) to show who this wallet interacts with.

📂 Repository & Data

• Model & Artifacts: uyen1109/eth-fraud-gnn-uyenuyen-v3
• Notebook Analysis: Based on btc2-3.ipynb.

⚠️ Disclaimer

This is a research project. The risk scores are probabilistic estimations based on historical patterns and should not be taken as absolute financial or legal advice.