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 quickly becoming the de-facto Machine Learning models for learning from Graph data and hereby infer missing information, such as, predicting labels of nodes or imputing missing edges.

The main goal of this tutorial is to communicate new research from the TF-GNN team this year, and help practitioners and researchers implement GNNs in a TensorFlow setting.

Tutorial Website (references & material): https://github.com/tensorflow/gnn/blob/main/examples/tutorials/icml_2023/README.md