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Computer Science > Machine Learning

arXiv:2207.03522 (cs)
[Submitted on 7 Jul 2022 (v1), last revised 23 Jul 2023 (this version, v2)]

Title:TF-GNN: Graph Neural Networks in TensorFlow

Authors:Oleksandr Ferludin, Arno Eigenwillig, Martin Blais, Dustin Zelle, Jan Pfeifer, Alvaro Sanchez-Gonzalez, Wai Lok Sibon Li, Sami Abu-El-Haija, Peter Battaglia, Neslihan Bulut, Jonathan Halcrow, Filipe Miguel Gonçalves de Almeida, Pedro Gonnet, Liangze Jiang, Parth Kothari, Silvio Lattanzi, André Linhares, Brandon Mayer, Vahab Mirrokni, John Palowitch, Mihir Paradkar, Jennifer She, Anton Tsitsulin, Kevin Villela, Lisa Wang, David Wong, Bryan Perozzi
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Abstract: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.
Subjects: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE); Social and Information Networks (cs.SI); Physics and Society (physics.soc-ph); Machine Learning (stat.ML)
Cite as: arXiv:2207.03522 [cs.LG]
  (or arXiv:2207.03522v2 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2207.03522

Submission history

From: Bryan Perozzi [view email]
[v1] Thu, 7 Jul 2022 18:34:34 UTC (149 KB)
[v2] Sun, 23 Jul 2023 21:18:29 UTC (238 KB)
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