Hands-On Graph Neural Networks Using Python
Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch
Published on 23. September 2024
354 pages
978-1-80461-070-1 (ISBN)
Description
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Edition
Digital original
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
US School Grade: College Graduate Student
Edition type
Digital original
Product notice
Reflowable
File size
15,41 MB
ISBN-13
978-1-80461-070-1 (9781804610701)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
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Maxime Labonne
Hands-On Graph Neural Networks Using Python
Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch
Book
04/2023
Packt Publishing
€84.00
Shipment within 3-4 weeks
Person
Labonne Maxime :
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.
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.
Content
Table of Contents
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
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
