Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

LinkedIn Learning

Advanced Graph Neural Networks

via LinkedIn Learning

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Explore graph neural networks (GNNs) in depth to unlock new potential in data analysis and modeling.

Syllabus

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

Taught by

Janani Ravi

Reviews

Start your review of Advanced Graph Neural Networks

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.