| --- |
| 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. |
