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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
Not yet published
Expected 9 Dec 26Master Graph Neural Networks from fundamentals to production. Learn DeepWalk, GCN, GAT, GraphSAGE, Graph Transformers, and more with hands-on Python implementations. New chapters on graph databases, LLMs, and foundation models.
Key FeaturesUnderstand and implement core GNN architectures including GCN, GAT, GraphSAGE, and Graph TransformersApply GNNs to real-world tasks like traffic forecasting, anomaly detection, recommender systems, and GraphRAGNew coverage of graph databases, Graph Transformers, LLM–GNN integration, and Graph Foundation ModelsBook DescriptionGraph 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 Graph Convolutional Networks, Graph Attention Networks, and GraphSAGE.
This edition introduces several new chapters reflecting the latest 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 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.
What you will learnMaster graph theory and manipulate graph data with NetworkX and PyGCreate node embeddings using DeepWalk and Node2VecImplement GCN, GAT, and GraphSAGE architectures from scratchBuild and train Graph Transformers on molecular datasetsConnect graph databases to PyG for scalable GNN pipelinesApply GNNs to traffic forecasting, anomaly detection, and recommendationsIntegrate GNNs with LLMs for graph-based retrieval-augmented generationUnderstand the emerging landscape of Graph Foundation ModelsWho this book is forThis book is for machine learning practitioners, data scientists, and researchers who want to learn how to apply deep learning to graph-structured data. Software engineers working with network data, knowledge graphs, or recommendation systems will also benefit. A working knowledge of Python and familiarity with basic machine learning concepts (neural networks, training loops, loss functions) is expected. Prior experience with PyTorch is helpful but not required, as key concepts are introduced progressively.
Table of ContentsGetting Started with Graph LearningGraph Theory for Graph Neural NetworksCreating Node Representations with DeepWalkImproving Embeddings with Biased Random Walks in Node2VecIncluding Node Features with Vanilla Neural NetworksIntroducing Graph Convolutional NetworksGraph Attention NetworksScaling Graph Neural Networks with GraphSAGEBridging Graph Databases and GNNs for ScalabilityGraph Tr
Key FeaturesUnderstand and implement core GNN architectures including GCN, GAT, GraphSAGE, and Graph TransformersApply GNNs to real-world tasks like traffic forecasting, anomaly detection, recommender systems, and GraphRAGNew coverage of graph databases, Graph Transformers, LLM–GNN integration, and Graph Foundation ModelsBook DescriptionGraph 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 Graph Convolutional Networks, Graph Attention Networks, and GraphSAGE.
This edition introduces several new chapters reflecting the latest 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 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.
What you will learnMaster graph theory and manipulate graph data with NetworkX and PyGCreate node embeddings using DeepWalk and Node2VecImplement GCN, GAT, and GraphSAGE architectures from scratchBuild and train Graph Transformers on molecular datasetsConnect graph databases to PyG for scalable GNN pipelinesApply GNNs to traffic forecasting, anomaly detection, and recommendationsIntegrate GNNs with LLMs for graph-based retrieval-augmented generationUnderstand the emerging landscape of Graph Foundation ModelsWho this book is forThis book is for machine learning practitioners, data scientists, and researchers who want to learn how to apply deep learning to graph-structured data. Software engineers working with network data, knowledge graphs, or recommendation systems will also benefit. A working knowledge of Python and familiarity with basic machine learning concepts (neural networks, training loops, loss functions) is expected. Prior experience with PyTorch is helpful but not required, as key concepts are introduced progressively.
Table of ContentsGetting Started with Graph LearningGraph Theory for Graph Neural NetworksCreating Node Representations with DeepWalkImproving Embeddings with Biased Random Walks in Node2VecIncluding Node Features with Vanilla Neural NetworksIntroducing Graph Convolutional NetworksGraph Attention NetworksScaling Graph Neural Networks with GraphSAGEBridging Graph Databases and GNNs for ScalabilityGraph Tr
366 pages, Paperback
Expected publication December 9, 2026
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