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TensorFlow GNN is a library to build Graph Neural Networks on the TensorFlow platform.

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TensorFlow GNN

Summary

TensorFlow GNN is a library to build Graph Neural Networks on the TensorFlow platform. It provides...

This library is an OSS port of a Google-internal library used in a broad variety of contexts, on homogeneous and heterogeneous graphs, and in conjunction with other scalable graph mining tools.

For background and discussion, please see O. Ferludin et al.: TF-GNN: Graph Neural Networks in TensorFlow, 2023 (full citation below).

Quickstart

Google Colab lets you run TF-GNN demos from your browser, no installation required:

For all colabs and user guides, please see the Documentation overview page, which also links to the API docs.

Installation Instructions

The latest stable release of TensorFlow GNN is available from

pip install tensorflow_gnn

For installation from source, see our Developer Guide.

Key platform requirements:

  • TensorFlow 2.12, 2.13, 2.14 or 2.15, and any GPU drivers it needs [instructions].
  • Keras v2, as traditionally included with TensorFlow 2.x. (TF-GNN does not work with the new multi-backend Keras v3.)
  • Apache Beam for distributed graph sampling.

TF-GNN is developed and tested on Linux. Running on other platforms supported by TensorFlow may be possible.

Citation

When referencing this library in a paper, please cite the TF-GNN paper:

@article{tfgnn,
  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 Gon{\c{c}}alves de Almeida and Pedro Gonnet and
             Liangze Jiang and Parth Kothari and Silvio Lattanzi and 
             Andr{\'{e}} 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},
  title   = {{TF-GNN:} Graph Neural Networks in TensorFlow},
  journal = {CoRR},
  volume  = {abs/2207.03522},
  year    = {2023},
  url     = {http://arxiv.org/abs/2207.03522},
}

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