Node and Edge Classification with GraphSAGE for Graph Machine Learning

Learn node and edge classification techniques using GraphSAGE in this 31-minute tutorial that provides a comprehensive implementation guide for Graph Machine Learning. Master Deep Graph Library (DGL) with PyTorch through hands-on code examples covering both homogeneous and heterogeneous graph structures. Explore message passing mechanisms in Graph Neural Networks and understand how they leverage node features along with neighboring node and edge information for classification tasks. Progress through practical implementations starting with basic DGL code, advancing to node classification, and culminating in edge classification for both simple and heterogeneous graphs. Follow along with code examples adapted from DGL's official documentation to build a strong foundation in graph-based machine learning applications.