Graph Neural Networks: Unlock a whole new range of deep learning applications with graph data

Title: Graph Neural Networks: Unlock a whole new range of deep learning applications with graph data
ISBN 10: 1804617520
ISBN 13: 9781804617526
Condition: New

About this title

Synopsis

Build previously impossible applications using graph data and become an expert in graph neural networks

Key Features

Learn the fundamentals of graph theory for data science and machine learning
Implement state-of-the-art graph neural network architectures
Build creative and powerful applications in various fields

Book Description

Only ten years after their creation, graph neural networks have become one of the most interesting architectures in deep learning. They have revolutionized multi-billion-dollar industries like drug discovery, where they predicted a brand-new antibiotic named Halicin. Tech companies are now trying to apply them everywhere: recommender systems for food, videos, and romantic partners; fake news detection, chip design, and 3D reconstruction.

In Graph Neural Networks, we will explore the fundamentals of graph theory and create our own datasets from raw or tabular data. We will introduce major graph neural network architectures to understand crucial concepts like graph convolution and self-attention. This knowledge will then be applied to understand and implement more specialized models, designed for various tasks (including link prediction and graph classification) or contexts (spatio-temporal data, heterogeneous graphs, and so on). Finally, we will solve real-life problems using this technology and start building a professional portfolio.

By the end of this book, you will become a Graph Neural Network expert. You will be able to reframe your problems to leverage the unreasonable effectiveness of this architecture. With these skills, you will create unique solutions using novel, state-of-the-art approaches.

What you will learn

Create your own graph datasets from tabular or raw data
Transform nodes and edges into high-quality embeddings
Implement graph neural networks using PyTorch Geometric
Select the best graph neural network model according to your problem
Perform tasks like node classification, graph generation, link prediction
Apply this knowledge to real use cases with raw data

Who This Book Is For

If you’re interested in machine learning, Graph Neural Networks will unlock a whole range of applications that were previously impossible. Students, data scientists, and machine learning and deep learning experts will find clear and illustrated explanations with code and notebooks to get a head start. With minimal knowledge of Python and linear algebra, you will acquire highly valuable expertise in one of the most popular architectures in AI.

Getting Started with Graph Learning
Graph Theory for Graph Neural Networks
Creating Node Representations with DeepWalk
Node2Vec
Vanilla Neural Network
Normalizing Features with Graph Convolutional Networks
Graph Attention Network
Scaling Graph Neural Networks
Graph Classification
Link Prediction
Graph Generation
Learning from Heterogeneous Graphs
Temporal Graph Neural Networks
Explainability
Traffic Forecasting
Anomaly Detection
Recommender Systems

About the Author

Maxime Labonne is currently a senior applied researcher at Airbus. He received a M.Sc. degree in computer science from INSA CVL, and a Ph.D. in machine learning and cyber security from the Polytechnic Institute of Paris. During his career, he worked on computer networks and the problem of representation learning, which led him to explore graph neural networks. He applied this knowledge to various industrial projects, including intrusion detection, satellite communications, quantum networks, and AI-powered aircrafts. He is now an active graph neural network evangelist through Twitter and his personal blog.

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