Graph Neural Networks and Deep Representation Learning

Graph data are highly irregular and non-Euclidean, meaning nodes have varying numbers of neighbors and no fixed ordering, which makes traditional deep learning models ineffective. This challenge motivates advanced deep representation learning and graph neural networks to be designed as a model for complex structures. A Graph Neural Network (GNNs) architecture with message-passing mechanisms was introduced, which allows nodes to aggregate information from their neighborhoods. Core concepts of Graph Convolutional Networks (GCNs) are discussed to explain how convolutional operations are adapted for graphs. The other architectures discussed are Graph Recurrent Neural Networks (GRNNs) and Graph Attention Networks (GATs). The chapter concludes with the emphasize on the role of deep graph learning in building intelligent network-based systems.