Advanced Graph Neural Networks

Explore graph neural networks (GNNs) in depth to unlock new potential in data analysis and modeling.

Introduction

• Overview of graph neural networks
• Prerequisites

1. Overview of Graph Neural Networks

• Message passing in GNNs
• Aggregation and transformation math
• Aggregation and transformation math in matrix form

2. Node Classification with Graph Attention Networks

• Introducing graph attention
• Computing the attention coefficient
• Including attention in GNN layers
• Getting set up with Colab and the PyTorch Geometric library
• Exploring the Cora dataset
• Setting up the graph convolutional network
• Training a graph convolutional network
• Node classification using a graph attention network
• Using the GATv2Conv layer for attention

3. Graph Classification Using Graph Convolution

• Understanding graph classification
• Exploring the PROTEINS Dataset for graph classification
• Minibatching graph data
• Setting up a graph classification model
• Training a GNN for graph classification
• Eliminating neighborhood normalization and skip connections

4. Link Prediction Using Graph Autoencoders

• A quick overview of autoencoders
• Introducing graph autoencoders
• Splitting link prediction data
• Understanding link splits
• Designing an autoencoder for link prediction
• Training the autoencoder

Conclusion

• Summary and next steps