MASTERING GRAPH MACHINE LEARNING

Build, train, and deploy graph neural networks with the two dominant frameworks — then take them to the frontier where graphs meet large language models. Graph-structured data is everywhere: social networks, molecules, road systems, transaction ledgers, knowledge bases, and recommenders. Mastering Graph Machine Learning is the practitioners guide to learning directly on that structure using PyTorch Geometric and DGL. Across 30 hands-on chapters, you will move from foundations to production: Core GNN architectures — GCN, GraphSAGE, GAT, GIN, and graph autoencoders, each with runnable code Advanced methods — spectral filters, graph transformers, heterogeneous and temporal graphs, and techniques to fight over-smoothing and over-squashing PyTorch Geometric deep dive — Data objects, custom datasets, NeighborLoader, mixed precision, and profiling at million-node scale DGL deep dive — update_all, custom message functions, and edge-gated layers LLMs meet graphs — text-attributed graphs, GraphRAG over knowledge graphs, and three concrete recipes for combining LLMs with GNNs Real applications — molecular property prediction with RDKit, LightGCN recommenders, fraud detection on transaction graphs, traffic forecasting, and anomaly detection Production — evaluation harnesses, inductive and temporal splits, FastAPI serving, embedding caches, and monitoring