Overview of graph neural networks

Prerequisites

Message passing in GNNs

Aggregation and transformation math

Aggregation and transformation math in matrix form

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

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

A quick overview of autoencoders

Splitting link prediction data

Understanding link splits

Designing an autoencoder for link prediction

Training the autoencoder

Summary and next steps