Exploring Graph Neural Networks

This chapter examines the intricacies of graph neural networks, focusing on how they distinguish between isomorphic and non-isomorphic graphs. The discussion includes the importance of metric geometry, Euclidean distance approximations, and convolution principles unique to graph structures. Additionally, it explores the integration of manifold learning with graph construction, particularly in healthcare applications, while forecasting the future of these technologies in various industries.