Link Prediction with Graph Neural Networks Using DGL and PyG

Overview

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.

Syllabus

CODE: GRAPH Link Prediction w/ DGL on Pytorch and PyG Code Example | GraphML | GNN

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