Dive deep into Graph Convolutional Networks (GCN) with this comprehensive 50-minute video lecture. Explore the most cited paper in GNN literature, covering all aspects of GCN from three different perspectives: spectral, Weisfeiler-Lehman, and Message Passing Neural Networks. Learn about Graph Laplacian regularization methods, in-depth GCN methodology, vectorized form explanations, and the spectral methods motivating GCNs. Visualize GCN hidden features using t-SNE, understand semi-supervised learning processes, and examine graph embedding methods and results. Compare GCN variations, analyze speed benchmarks and limitations, and investigate the Weisfeiler-Lehman perspective, contrasting GCN with Graph Isomorphism Networks (GIN). Gain insights into Graph Attention Networks (GAT) and explore the consequences of the Weisfeiler-Lehman test on GNN architectures and depth.