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Hands-On Graph Neural Networks Using Python. Build, train, and optimize graph-based deep learning models with PyTorch Geometric and Python - Second Edition
Giuseppe Futia, Maxime Labonne
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Graph Neural Networks have become essential tools for learning from relational and structured data. This second edition provides a comprehensive, hands-on guide implementing GNNs using Python and PyTorch Geometric.
The book begins with graph theory fundamentals and data manipulation using NetworkX and PyTorch Geometric. You then explore shallow embedding methods—DeepWalk and Node2Vec—before progressing to core GNN architectures: Graph Convolutional Networks, Graph Attention Networks, and GraphSAGE.
This edition introduces several new chapters reflecting the latest advances: Graph Transformers, integration of graph databases with GNNs, the convergence of LLMs and GNNs through GraphRAG, and the emerging paradigm of Graph Foundation Models. Existing chapters on expressiveness, link prediction, heterogeneous graphs, temporal GNNs, and explainability have been updated with current best practices.
The final part puts theory into practice with end-to-end projects: traffic forecasting with A3T-GCN, anomaly detection with heterogeneous GNNs, and building a recommender system with LightGCN.
By the end of this book, you will have the knowledge and practical skills to apply GNNs to your own graph-structured data problems.
The book begins with graph theory fundamentals and data manipulation using NetworkX and PyTorch Geometric. You then explore shallow embedding methods—DeepWalk and Node2Vec—before progressing to core GNN architectures: Graph Convolutional Networks, Graph Attention Networks, and GraphSAGE.
This edition introduces several new chapters reflecting the latest advances: Graph Transformers, integration of graph databases with GNNs, the convergence of LLMs and GNNs through GraphRAG, and the emerging paradigm of Graph Foundation Models. Existing chapters on expressiveness, link prediction, heterogeneous graphs, temporal GNNs, and explainability have been updated with current best practices.
The final part puts theory into practice with end-to-end projects: traffic forecasting with A3T-GCN, anomaly detection with heterogeneous GNNs, and building a recommender system with LightGCN.
By the end of this book, you will have the knowledge and practical skills to apply GNNs to your own graph-structured data problems.
- 1. Getting Started with Graph Learning
- 2. Graph Theory for Graph Neural Networks
- 3. Creating Node Representations with DeepWalk
- 4. Improving Embeddings with Biased Random Walks in Node2Vec
- 5. Including Node Features with Vanilla Neural Networks
- 6. Introducing Graph Convolutional Networks
- 7. Graph Attention Networks
- 8. Scaling Graph Neural Networks with GraphSAGE
- 9. Bridging Graph Databases and GNNs for Scalability
- 10. Graph Transformers - Attention Beyond Local Neighborhoods
- 11. Defining Expressiveness for Graph Classification
- 12. Predicting Links with Graph Neural Networks
- 13. Learning from Heterogeneous Graphs
- 14. Temporal Graph Neural Networks
- 15. Explaining Graph Neural Networks
- 16. Forecasting Traffic Using A3T-GCN
- 17. Detecting Anomalies Using Heterogeneous Graph Neural Networks
- 18. Building a Recommender System Using LightGCN
- 19. Large Language Models Meet Graph Neural Networks
- 20. Unlocking the Potential of Graph Foundation Models
- Title:Hands-On Graph Neural Networks Using Python. Build, train, and optimize graph-based deep learning models with PyTorch Geometric and Python - Second Edition
- Author:Giuseppe Futia, Maxime Labonne
- Original title:Hands-On Graph Neural Networks Using Python. Build, train, and optimize graph-based deep learning models with PyTorch Geometric and Python - Second Edition
- ISBN:9781806382705, 9781806382705
- Date of issue:2026-11-20
- Format:Ebook - EPUB
- Item ID: e_4xa1
- Publisher: Packt Publishing
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