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.