Explore graph neural networks (GNNs) in this MIT Deep Learning lecture that demonstrates how these architectures connect to multilayer perceptrons and convolutional neural networks through message passing algorithms. Examine the theoretical foundations of GNN expressive power, including their inherent limitations and what these constraints mean for real-world applications. Learn how graph structures can be processed using neural network approaches, with detailed analysis of message passing mechanisms that enable information flow across graph nodes and edges. Understand the mathematical frameworks that govern GNN capabilities and discover the practical implications of theoretical limitations when designing graph-based machine learning systems.