AstroML

Dynamic Graph Machine Learning Framework for the Stellar Network

AstroML is a research-driven Python framework for building dynamic graph machine learning models on the Stellar Development Foundation Stellar blockchain.

It treats blockchain data as a multi-asset, time-evolving graph, enabling advanced ML research on transaction networks such as fraud detection, anomaly detection, and behavioral modeling.

---

✨ Features

AstroML provides end-to-end tooling for:

• Ledger ingestion and normalization
• Dynamic transaction graph construction
• Feature engineering for blockchain accounts
• Graph Neural Networks (GNNs)
• Self-supervised node embeddings
• Anomaly detection
• Temporal modeling
• Reproducible ML experimentation

---

🧠 Core Idea

Blockchain networks are naturally graph-structured systems:

Blockchain Concept	Graph Representation
Accounts	Nodes
Transactions	Directed edges
Assets	Edge types
Time	Dynamic dimension

Most analytics tools rely on static heuristics or SQL queries.

AstroML instead enables:

• Dynamic graph learning
• Temporal GNNs
• Representation learning
• Research-grade experimentation

---

🎯 Target Users

AstroML is designed for:

• ML researchers
• Graph ML engineers
• Fraud detection teams
• Blockchain data scientists

---

🏗 Architecture Overview

Ledger → Ingestion → Normalization → Graph Builder → Features → GNN/ML Models → Experiments

🚀 Getting Started

1. Clone the repository

git clone https://github.com/Traqora/astroml.git
cd astroml

2. Create environment

python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

3. Configure database

Create a PostgreSQL database and update:

config/database.yaml

---

📥 Data Ingestion

Backfill ledgers:

python -m astroml.ingestion.backfill \
  --start-ledger 1000000 \
  --end-ledger 1100000

---

🕸 Build Graph Snapshot

Create a rolling time window graph:

python -m astroml.graph.build_snapshot --window 30d

---

🧪 Synthetic Fraud Pattern Injection

Create benchmark datasets by injecting controlled fraud structures into a clean ledger copy:

python -m astroml.ingestion.synthetic_fraud_injector \
  --input data/clean_ledger.jsonl \
  --output data/ledger_with_fraud.jsonl \
  --summary outputs/fraud_injection_summary.json \
  --sybil-clusters 3 \
  --sybil-cluster-size 8 \
  --wash-loops 2 \
  --wash-loop-size 5

The injector appends transactions tagged with synthetic_fraud=true and fraud_pattern (sybil_cluster or wash_trading_loop) for downstream benchmarking.

---

🤖 Train Baseline GCN

python -m astroml.training.train_gcn

---

📊 Example Use Cases

• Liquidity Monitoring for the Stellar Community Fund
• Fraud / scam detection
• Account clustering
• Transaction risk scoring
• Temporal behavior modeling
• Self-supervised embeddings
• Network anomaly detection

---

🔬 Research Goals

AstroML emphasizes:

• Reproducibility
• Modular experimentation
• Scalable ingestion
• Temporal graph learning
• Production-ready ML pipelines

---

🛠 Tech Stack

• Python
• PyTorch / PyTorch Geometric
• PostgreSQL
• NetworkX / graph tooling

---

📌 Roadmap

• Real-time streaming ingestion
• Temporal GNN models
• Contrastive learning pipelines
• Feature store
• Model benchmarking suite
• Docker deployment

---

🤝 Contributing

Contributions are welcome!

fork → branch → commit → PR

Please open issues for bugs or feature requests.

---

📜 License

MIT License