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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.