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LinkedIn Learning

Advanced Graph Neural Networks

via LinkedIn Learning

Overview

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

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