Opis
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.