# UHG Library Improvements

## Completed Improvements

1. ✅ Feature Extraction
- Implemented UHG-based feature extraction
- Added geometric pattern recognition
- Optimized for cybersecurity use cases

2. ✅ Anomaly Detection
- Added UHG anomaly scoring
- Implemented geometric distance metrics
- Optimized for outlier detection

3. ✅ Metric Learning
- Implemented UHG metric learning
- Added similarity preservation
- Optimized for pattern matching

4. ✅ Attention Mechanisms
- Implemented UHG attention
- Added multi-head attention
- Preserved geometric invariants

5. ✅ Threat Correlation
- Added indicator correlation
- Implemented relationship analysis
- Added cross-type pattern recognition

## Planned Improvements

6. 🔄 Graph Neural Networks (Next Priority)
- Implement UHG graph layers
- Add message passing functions
- Support graph attention
- Preserve hyperbolic structure

7. 📋 Visualization Tools
- Add UHG space visualization
- Implement pattern visualization
- Support relationship graphs
- Interactive exploration tools

8. 📋 Performance Optimization
- GPU acceleration
- Batch processing
- Memory optimization
- Sparse operations

9. 📋 Integration Tools
- STIX/TAXII support
- MISP integration
- Elastic integration
- Splunk integration

10. 📋 Documentation
- API documentation
- Usage examples
- Best practices
- Performance guide

## Technical Details

### Next Priority: Graph Neural Networks

Required Components:
1. UHG Graph Layers
- Message passing in projective space
- Cross-ratio preserving aggregation
- Geometric feature updates

2. Graph Attention
- UHG-based attention mechanism
- Multi-head graph attention
- Relationship preservation

3. Graph Operations
- Node feature updates
- Edge feature updates
- Graph pooling
- Graph readout

4. Graph Learning
- Node classification
- Edge prediction
- Graph classification
- Pattern matching

Implementation Plan:
1. Core graph operations
2. Message passing functions
3. Attention mechanisms
4. Learning objectives

Benefits:
- Better pattern recognition
- Improved relationship learning
- Enhanced threat detection
- Scalable to large graphs

### Future Considerations

1. Advanced Features
- Dynamic graphs
- Temporal patterns
- Multi-modal data
- Hierarchical structures

2. Optimization
- Distributed processing
- Model compression
- Incremental learning
- Online updates

3. Integration
- Cloud deployment
- Container support
- API services
- Monitoring tools

4. Security
- Model protection
- Data privacy
- Access control
- Audit logging