This is the Graph Neural Networks: Hands-on Session from the Stanford 2019 Fall CS224W course.

In this tutorial, we will explore the implementation of graph neural networks and investigate what representations these networks learn. Along the way, we'll see how PyTorch Geometric and TensorBoardX can help us with constructing and training graph models.

Pytorch Geometric tutorial part starts at -- 0:33:30

Details on: * Graph Convolutional Neural Networks (GCN) * Custom Convolutional Model * Message passing * Aggregation functions * Update * Graph Pooling