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TensorFlow GNN\n\n## Summary\n\nTensorFlow GNN is a library to build\n[Graph Neural Networks](tensorflow_gnn/docs/guide/intro.md) on the TensorFlow platform.\nIt provides...\n\n  * a [`tfgnn.GraphTensor`](tensorflow_gnn/docs/guide/graph_tensor.md) type to represent\n    graphs with a [heterogeneous schema](tensorflow_gnn/docs/guide/schema.md), that is,\n    multiple types of nodes and edges;\n  * tools for [data preparation](tensorflow_gnn/docs/guide/data_prep.md),\n    notably a [graph sampler](tensorflow_gnn/docs/guide/beam_sampler.md)\n    to convert a huge database into a stream of reasonably-sized subgraphs for\n    training and inference;\n  * a collection of [ready-to-use models](tensorflow_gnn/models/README.md)\n    and Keras layers to do your own [GNN modeling](tensorflow_gnn/docs/guide/gnn_modeling.md);\n  * a high-level API for training [orchestration](tensorflow_gnn/docs/guide/runner.md).\n\nThis library is an OSS port of a Google-internal library used in a broad variety\nof contexts, on homogeneous and heterogeneous graphs, and in conjunction with\nother scalable graph mining tools.\n\nFor background, please see our\n[blog post](https://blog.tensorflow.org/2024/02/graph-neural-networks-in-tensorflow.html)\nand the [TF-GNN paper](https://arxiv.org/abs/2207.03522) (full citation below).\n\n## Quickstart\n\nGoogle Colab lets you run TF-GNN demos from your browser, no installation\nrequired:\n\n  * [Molecular Graph\n    Classification](https://colab.research.google.com/github/tensorflow/gnn/blob/master/examples/notebooks/intro_mutag_example.ipynb)\n     with the MUTAG dataset.\n  * [Solving OGBN-MAG\n    end-to-end](https://colab.research.google.com/github/tensorflow/gnn/blob/master/examples/notebooks/ogbn_mag_e2e.ipynb)\n    trains a model on heterogeneous sampled subgraphs from the popular\n    [OGBN-MAG](https://ogb.stanford.edu/docs/nodeprop/#ogbn-mag) benchmark.\n  * [Learning shortest paths with\n    GraphNetworks](https://colab.research.google.com/github/tensorflow/gnn/blob/master/examples/notebooks/graph_network_shortest_path.ipynb)\n    demonstrates an advanced Encoder/Process/Decoder architecture for predicting\n    the edges of a shortest path.\n\nFor all colabs and user guides, please see the\n[Documentation overview](tensorflow_gnn/docs/guide/overview.md)\npage, which also links to the\n[API docs](tensorflow_gnn/docs/api_docs/README.md).\n\n## Installation Instructions\n\nThe latest stable release of TensorFlow GNN is available from\n\n```\npip install tensorflow-gnn\n```\n\nFor installation from source, see our [Developer\nGuide](tensorflow_gnn/docs/guide/developer.md).\n\nKey platform requirements:\n\n  * TensorFlow 2.12 or higher, and any GPU drivers it needs\n    [[instructions](https://www.tensorflow.org/install)].\n  * Keras v2, as traditionally included with TensorFlow 2.x.\n    TF-GNN does not work with the new multi-backend Keras v3.\u003cbr/\u003e\n    **Users of TF2.16+ must also `pip install tf-keras` and set\n    TF_USE_LEGACY_KERAS=1**,\n    see our [Keras version](tensorflow_gnn/docs/guide/keras_version.md) guide for details.\n  * Apache Beam for distributed graph sampling.\n  * For some tests or scripts that requires tensorflow.lite it is required to\n    install ai-edge-litert by using `pip install ai-edge-litert`\n\nTF-GNN is developed and tested on Linux. Running on other platforms supported\nby TensorFlow may be possible.\n\n## Citation\n\nWhen referencing this library in a paper, please cite the\n[TF-GNN paper](https://arxiv.org/abs/2207.03522):\n\n```\n@article{tfgnn,\n  author  = {Oleksandr Ferludin and Arno Eigenwillig and Martin Blais and\n             Dustin Zelle and Jan Pfeifer and Alvaro Sanchez{-}Gonzalez and\n             Wai Lok Sibon Li and Sami Abu{-}El{-}Haija and Peter Battaglia and\n             Neslihan Bulut and Jonathan Halcrow and\n             Filipe Miguel Gon{\\c{c}}alves de Almeida and Pedro Gonnet and\n             Liangze Jiang and Parth Kothari and Silvio Lattanzi and \n             Andr{\\'{e}} Linhares and Brandon Mayer and Vahab Mirrokni and\n             John Palowitch and Mihir Paradkar and Jennifer She and\n             Anton Tsitsulin and Kevin Villela and Lisa Wang and David Wong and\n             Bryan Perozzi},\n  title   = {{TF-GNN:} Graph Neural Networks in TensorFlow},\n  journal = {CoRR},\n  volume  = {abs/2207.03522},\n  year    = {2023},\n  url     = {http://arxiv.org/abs/2207.03522},\n}\n```\n","funding_links":[],"categories":["Neural Networks (NN) and Deep Neural Networks (DNN)","Graph Machine Learning","Python"],"sub_categories":["NN/DNN Models","Others"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftensorflow%2Fgnn","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftensorflow%2Fgnn","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftensorflow%2Fgnn/lists"}