Book

Hands-On Graph Neural Networks Using Python

Hands-On Graph Neural Networks Using Python is a comprehensive guide to building and training graph neural networks for a variety of real-world applications. With clear explanations and plenty of hands-on examples, this book is a valuable resource for anyone looking to learn about and apply graph neural networks to their own projects.

Offered byPackt Logo

Difficulty Level

Intermediate

Completion Time

11h48m

Language

English

About Book

Who Is This Book For?

This book is for machine learning practitioners and data scientists interested in learning about graph neural networks and their applications, as well as students looking for a comprehensive reference on this rapidly growing field. Whether you’re new to graph neural networks or looking to take your knowledge to the next level, this book has something for you. Basic knowledge of machine learning and Python programming will help you get the most out of this book.

Book content

chapters 11h48m total length

Getting Started with Graph Learning

Graph Theory for Graph Neural Networks

Creating Node Representations with DeepWalk

Improving Embeddings with Biased Random Walks in Node2Vec

Including Node Features with Vanilla Neural Networks

Introducing Graph Convolutional Networks

Graph Attention Networks

Scaling Graph Neural Networks with GraphSAGE

Defining Expressiveness for Graph Classification

Predicting Links with Graph Neural Networks

Generating Graphs Using Graph Neural Networks

Learning from Heterogeneous Graphs

Temporal Graph Neural Networks

Explaining Graph Neural Networks

Forecasting Traffic Using A3T-GCN

Detecting Anomalies Using Heterogeneous Graph Neural Networks

Building a Recommender System Using LightGCN

Unlocking the Potential of Graph Neural Networks for Real-Word Applications

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