---
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](https://huggingface.co/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.