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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