  TensorFlow-GNN (TF-GNN) is a scalable library for Graph Neural Networks in
TensorFlow. It is designed from the bottom up to support the kinds of rich
heterogeneous graph data that occurs in today's information ecosystems. In
addition to enabling machine learning researchers and advanced developers,
TF-GNN offers low-code solutions to empower the broader developer community in
graph learning. Many production models at Google use TF-GNN, and it has been
recently released as an open source project. In this paper we describe the
TF-GNN data model, its Keras message passing API, and relevant capabilities
such as graph sampling and distributed training.
