Advanced Graph Neural Networks

Handling high-dimensional data is challenging because it increases computational cost, causes sparsity, and makes learning meaningful patterns and generalization more difficult. This chapter deals with the complex, dynamic, and high-dimensional graph data using advanced Graph Neural Network (GNN) models. It begins with Spatial-Temporal Graph Neural Networks (ST-GNNs), which integrate spatial dependencies and temporal dynamics to model time-evolving networks such as traffic and sensor systems. The chapter then explores Dynamic Graph Neural Networks that capture structural and feature changes over time, including models such as Temporal Graph Attention Networks (TGAT) and Dynamic Graph Convolutional Networks (DyGCN). Hypergraph Neural Networks are introduced to represent higher-order relationships beyond pairwise connections. In addition, unsupervised deep learning approaches, including Graph Autoencoders (GAE) and Variational Graph Autoencoders (VGAE), are discussed for learning latent representations and detecting patterns in graphs. The chapter concludes by highlighting how these advanced models enhance scalability, and predictive performance in real-world graph learning applications.