Graph Neural Networks (GNNs) are advanced machine learning models specifically designed to process and analyze data structured as graphs, making them particularly effective for detecting patterns in interconnected data like transaction networks and social relationships.
Key Features
- Network analysis
- Pattern recognition
- Relationship learning
- Node classification
- Edge prediction
Applications
- Fraud detection
- Network analysis
- Risk assessment
- Anomaly detection
- Relationship mapping
Capabilities
- Pattern identification
- Link prediction
- Node classification
- Community detection
- Structural analysis
Implementation Areas
- Transaction monitoring
- Social networks
- Payment systems
- Identity verification
- Risk management
Best Practices
- Data preparation
- Model optimization
- Feature selection
- Performance monitoring
- Regular updates
GNNs excel at identifying complex patterns and relationships in interconnected data structures.