Graph Neural Networks: Extending Deep Learning to Graphs

This chapter introduces graph neural networks (GNNs) and their importance in various domains. It covers the fundamentals of graph theory, representing graph data with tensors, and different GNN architectures like GCNs, GATs, and GraphSAGE. Training and optimization techniques for GNNs are discussed, along with practical applications in social network analysis, recommendation systems, and molecular biology. Implementation examples using TensorFlow and PyTorch, along with case studies and exercises, are provided.