graph_learning_tutorial_EN.md

AGL Graph Learning Tutorial

Assuming you have gone through the process of graph sample construction in AGL and successfully built the samples, this document will continue to explain how to perform graph learning tasks based on the constructed GraphFeatures.

Taking the classic node classification task on PPI data as an example, we will explore the AGL graph learning process from the following aspects:

• 1.IO and Dataset
• 2.Data Parsing
• 3.Model

If you prefer to dive in directly, you can also refer to the readme in the provided GeniePath on PPI example to run the data generation and graph learning tasks for the respective example. Below are the links to the relevant examples:

• GeniePath
• Drgst
• HeGNN
• Kcan
• Merit
• Nasa
• PaGNN
• SSR