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Hands-On Graph Neural Networks Using Python

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
Author (1):
Maxime Labonne Maxime Labonne
Profile icon 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
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Part 3: Advanced Techniques

In this third part of the book, we will delve into the more advanced and specialized GNN architectures that have been developed to solve a variety of graph-related problems. We will cover state-of-the-art GNN models designed for specific tasks and domains, which can address challenges and requirements more effectively. In addition, we will provide an overview of several new graph-based tasks that can be tackled using GNNs, such as link prediction and graph classification, and demonstrate their applications through practical code examples and implementations.

By the end of this part, you will be able to understand and implement advanced GNN architectures and apply them to solve your own graph-based problems. You will have a comprehensive understanding of specialized GNNs and their respective strengths, as well as hands-on experience with code examples. This knowledge will equip you with the skills to apply GNNs to real-world use cases and potentially contribute...

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Hands-On Graph Neural Networks Using Python
Published in: Apr 2023 Publisher: Packt ISBN-13: 9781804617526
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