TensorFlow GNN

Summary

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

• a tfgnn.GraphTensor type to represent
  graphs with a heterogeneous schema, that is,
  multiple types of nodes and edges;
• tools for data preparation,
  notably a graph sampler
  to convert a huge database into a stream of reasonably-sized subgraphs for
  training and inference;
• a collection of ready-to-use models
  and Keras layers to do your own GNN modeling;
• a high-level API for training orchestration.

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, please see our
blog post
and the TF-GNN paper (full citation below).

Quickstart

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

• Molecular Graph
  Classification
  with the MUTAG dataset.
• Solving OGBN-MAG
  end-to-end
  trains a model on heterogeneous sampled subgraphs from the popular
  OGBN-MAG benchmark.
• Learning shortest paths with
  GraphNetworks
  demonstrates an advanced Encoder/Process/Decoder architecture for predicting
  the edges of a shortest path.

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 (as of release 1.0):

• TensorFlow 2.12 or higher, and any GPU drivers it needs
  [instructions].
  (Future releases will raise the required TF version.)
• Keras v2, as traditionally included with TensorFlow 2.x.
  TF-GNN does not work with the new multi-backend Keras v3.<br/>
  Users of TF2.16+ must also pip install tf-keras and set
  TF_USE_LEGACY_KERAS=1,
  see our Keras version guide for details.
• Apache Beam for distributed graph sampling.
• For some tests or scripts that requires tensorflow.lite it is required to
  install ai-edge-litert by using pip install ai-edge-litert

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},
}