Labonne Hands-On Graph Neural Networks Using Python

Practical techniques and architectures for building powerful graph and deep learning apps with PyTorch
1. Auflage 2024
ISBN: 978-1-80461-070-1
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 354 Seiten

No detailed description available for "Hands-On Graph Neural Networks Using Python".

Autoren/Hrsg.

Labonne, Maxime

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Programmier- und Skriptsprachen
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Neuronale Netzwerke

Inhaltsverzeichnis

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

Über Autor(innen)

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.