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.
@article{arxiv.2207.03522,
title = {TF-GNN: Graph Neural Networks in TensorFlow},
author = {Oleksandr Ferludin and Arno Eigenwillig and Martin Blais and Dustin Zelle and Jan Pfeifer and Alvaro Sanchez-Gonzalez and Wai Lok Sibon Li and Sami Abu-El-Haija and Peter Battaglia and Neslihan Bulut and Jonathan Halcrow and Filipe Miguel Gonçalves de Almeida and Pedro Gonnet and Liangze Jiang and Parth Kothari and Silvio Lattanzi and André Linhares and Brandon Mayer and Vahab Mirrokni and John Palowitch and Mihir Paradkar and Jennifer She and Anton Tsitsulin and Kevin Villela and Lisa Wang and David Wong and Bryan Perozzi},
journal= {arXiv preprint arXiv:2207.03522},
year = {2023}
}