This repository provides a PyTorch implementation of Graph Convolutional Networks (GCNs), a deep learning approach for semi-supervised learning on graph-structured data. The implementation follows the architecture described in the landmark 2016 paper by Kipf and Welling, enabling node-level classification tasks by leveraging graph structure through message passing over adjacency matrices. It uses the Cora citation network dataset for demonstration.