ch18

Chapter 18: Graph Neural Networks for Capturing Dependencies in Graph Structured Data

Chapter Outline

• Introduction to graph data
  • Undirected graphs
  • Directed graphs
  • Labeled graphs
  • Representing molecules as graphs
• Understanding graph convolutions
  • The motivation behind using graph convolutions
  • Implementing a basic graph convolution
• Implementing a GNN in PyTorch from scratch
  • Defining the NodeNetwork model
  • Coding the NodeNetwork’s graph convolution layer
  • Adding a global pooling layer to deal with varying graph sizes
  • Preparing the DataLoader
  • Using the NodeNetwork to make predictions
• Implementing a GNN using the PyTorch Geometric library
• Other GNN layers and recent developments
  • Spectral graph convolutions
  • Pooling
  • Normalization
  • Pointers to advanced graph neural network literature
• Summary

Please refer to the README.md file in ../ch01 for more information about running the code examples.