Learn to implement link prediction on graph datasets using Deep Graph Library (DGL) and PyTorch Geometric (PyG) in this 21-minute tutorial. Explore the fundamental concepts of link prediction and develop practical coding skills through hands-on examples in both frameworks. Master techniques for representing node connectivity likelihood using GNN-based models, and understand how negative sampling compares edge scores between connected nodes against arbitrary node pairs. Dive into framework-agnostic implementation approaches that work with both PyG and TensorFlow2, while building upon GraphSAGE concepts for effective graph neural network applications.