Expo Workshop
Room 292

Tutorial Website is here!

Google Meet Link: https://meet.google.com/yko-cpuk-czg

Slides: https://drive.google.com/file/d/1ECcnRgJqjmj7hlegYuPdscLCUw7YJc7G/view?usp=sharing


Graphs are general data structures that can represent information from a variety of domains (social, biomedical, online transactions, and many more). Graph Neural Networks (GNNs) are an exciting way to use graph structured data inside neural network models that have recently exploded in popularity. However, implementing GNNs and running GNNs on large (and complex) datasets still raises a number of challenges for machine learning platforms.


The main goal of this tutorial is to help practitioners and researchers to implement GNNs in a TensorFlow setting. Specifically, the tutorial will be mostly hands-on, and will walk the audience through a process of running existing GNNs on heterogeneous graph data, and a tour of how to implement new GNN models. The hands-on portion of the tutorial will be based on TF-GNN, a new framework that we open-sourced.

Learning objectives
1. Conceptual understanding of Graph Neural Networks (GNNs).
2. Hands-on: How to train and evaluate GNNs in TensorFlow, using TF-GNN.
3. Understanding of message passing building blocks for crafting advanced GNN architectures.
4. Hands-on: How to implement custom models inside TF-GNN.
5. Know how to run TF-GNN models at scale, using cloud environments.
6. Hands-on: Run TF-GNN at scale on large graphs.


This tutorial consists of 3 lectures, paired with 3 python notebooks, which cover different aspects of working with TF-GNN.

- 9:30 AM. Basics of TF-GNN
- 10:30 AM. Modeling with TF-GNN
- 11:30 AM. Scaling GNNs w/ TF-GNN


ALL Presentation Slides

The notebooks (and recordings eventually) can be found on the tutorial website

Additional Resources

If you're interested in learning more about GNNs or TF-GNN, we recommend the following resources:

- Our paper TF-GNN: Graph Neural Networks in TensorFlow, details the API design and background of the library.
- The in-depth notebook OGBN-MAG end-to-end with TF-GNN offers a deep dive on building heterogeneous graph models using TF-GNN.