Learn to implement Graph Convolutional Networks (GCN) for node classification tasks in a 21-minute coding tutorial that demonstrates PyG (PyTorch Geometric) implementation. Explore how GCN architecture leverages localized first-order approximation of spectral graph convolutions to efficiently process graph data, scaling linearly with the number of edges while capturing both local graph structure and node features in hidden layer representations. Based on the seminal work by Kipf and Welling, discover practical implementation details for semi-supervised classification tasks using this foundational graph neural network architecture.