Graph Neural Networks represent a fundamental advancement in deep learning, enabling neural networks to directly process graph-structured data where entities (nodes) and their relationships (edges) carry semantic meaning. Unlike traditional neural networks that operate on grid-like structures (images, sequences), GNNs exploit the topology and connectivity patterns inherent in graphs through message passing — iteratively aggregating information from neighboring nodes to update node representations. GNNs have become the standard architecture for learning from networks in molecular chemistry, social systems, knowledge graphs, and any domain where relationships between entities are as important as the entities themselves. A critical insight: most GNN architectures are permutation equivariant (node order doesn't matter) yet often limited by the Weisfeiler-Leman test in their ability to distinguish certain graph structures.