You're reading from Hands-On Graph Neural Networks Using Python

Product type Book

Published in Apr 2023

Publisher Packt

ISBN-13 9781804617526

Pages 354 pages

Edition 1st Edition

Languages

Concepts

Neural Networks

Author (1):

Maxime Labonne

Table of Contents (25) Chapters

Preface

Part 1: Introduction to Graph Learning

Chapter 1: Getting Started with Graph Learning

Chapter 2: Graph Theory for Graph Neural Networks

Chapter 3: Creating Node Representations with DeepWalk

Part 2: Fundamentals

Chapter 4: Improving Embeddings with Biased Random Walks in Node2Vec

Chapter 5: Including Node Features with Vanilla Neural Networks

Chapter 6: Introducing Graph Convolutional Networks

Chapter 7: Graph Attention Networks

Part 3: Advanced Techniques

Chapter 8: Scaling Up Graph Neural Networks with GraphSAGE

Chapter 9: Defining Expressiveness for Graph Classification

Chapter 10: Predicting Links with Graph Neural Networks

Chapter 11: Generating Graphs Using Graph Neural Networks

Chapter 12: Learning from Heterogeneous Graphs

Chapter 13: Temporal Graph Neural Networks

Chapter 14: Explaining Graph Neural Networks

Part 4: Applications

Chapter 15: Forecasting Traffic Using A3T-GCN

Chapter 16: Detecting Anomalies Using Heterogeneous GNNs

Chapter 17: Building a Recommender System Using LightGCN

Chapter 18: Unlocking the Potential of Graph Neural Networks for Real-World Applications

Index

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Introducing GraphSAGE

Hamilton et al. introduced GraphSAGE in 2017 (see item [1] of the Further reading section) as a framework for inductive representation learning on large graphs (with over 100,000 nodes). Its goal is to generate node embeddings for downstream tasks, such as node classification. In addition, it solves two issues with GCNs and GATs – scaling to large graphs and efficiently generalizing to unseen data. In this section, we will explain how to implement it by describing the two main components of GraphSAGE:

Neighbor sampling
Aggregation

Let’s take a look at them.

Neighbor sampling

So far, we haven’t discussed an essential concept in traditional neural networks – mini-batching. It consists of dividing our dataset into smaller fragments, called batches. They are used in gradient descent, the optimization algorithm that finds the best weights and biases during training. There are three types of gradient descent:

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