Graph Neural Network

Neural networks that process data structured as graphs

Overview

Graph Neural Networks (GNNs) are specialized AI models that analyze and learn from data represented as graphs, where information exists in both nodes and their connections. These networks excel at understanding complex relationships in interconnected data structures.

GNNs provide:

  • Node-level analysis
  • Edge relationship processing
  • Graph structure learning
  • Pattern recognition
  • Relationship inference
  • Network-wide understanding

Applications

Common uses include:

  • Social network analysis
  • Molecular structure prediction
  • Knowledge graph processing
  • Recommendation systems
  • Drug discovery
  • Traffic flow modeling

Technical Components

Core elements:

  • Message passing functions
  • Node update mechanisms
  • Edge feature processing
  • Aggregation functions
  • Graph-level outputs
  • Learning algorithms