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‎GNNGraphs/README.md‎

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# GNNGraphs.jl
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[![](https://img.shields.io/badge/docs-stable-blue.svg)](https://juliagraphs.org/GraphNeuralNetworks.jl/docs/GraphNeuralNetworks.jl/)
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A package implementing graph types for graph deep learning.
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This package is currently under development and may break frequentely.
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It is not meant for final users but for GNN libraries developers.
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Final user should use GraphNeuralNetworks.jl instead.
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The package is part of the [GraphNeuralNetworks.jl ecosystem](https://github.com/JuliaGraphs/GraphNeuralNetworks.jl) and is re-exported by the frontend packages [GraphNeuralNetworks.jl](https://juliagraphs.org/GraphNeuralNetworks.jl/docs/GraphNeuralNetworks.jl/) and [GNNLux.jl](https://juliagraphs.org/GraphNeuralNetworks.jl/docs/GNNLux.jl/).
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## Installation
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Install through the Julia package manager.
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```julia
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pkg> add GNNGraphs
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```
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## Usage
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For a comprehensive introduction to the library, refer to the [Documentation](https://juliagraphs.org/GraphNeuralNetworks.jl/docs/GNNGraphs.jl/).
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## Citing
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If you use GraphNeuralNetworks.jl in a scientific publication, we would appreciate a reference
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to [our paper](https://arxiv.org/abs/2412.06354):
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```
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@article{lucibello2024graphneuralnetworks,
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title={GraphNeuralNetworks.jl: Deep Learning on Graphs with Julia},
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author={Lucibello, Carlo and Rossi, Aurora},
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journal={arXiv preprint arXiv:2412.06354},
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url={https://arxiv.org/abs/2412.06354},
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year={2024}
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}
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```
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‎GNNLux/README.md‎

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<img align="right" width="300px" src="https://raw.githubusercontent.com/JuliaGraphs/GraphNeuralNetworks.jl/master/docs/logo.svg">
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# GNNLux.jl
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[![](https://img.shields.io/badge/docs-stable-blue.svg)](https://juliagraphs.org/GraphNeuralNetworks.jl/docs/GNNLux.jl/)
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Graph convolutional layers based on the deep learning framework [Lux.jl](https://lux.csail.mit.edu/stable/).
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This is the frontend package for Lux users of the [GraphNeuralNetworks.jl ecosystem](https://github.com/JuliaGraphs/GraphNeuralNetworks.jl).
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### Features
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**GNNLux.jl** supports the following features:
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- Implementation of common graph convolutional layers.
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- Computation on batched graphs.
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- Custom layer definitions.
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- Support for CUDA and AMDGPU.
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- Integration with [Graphs.jl](https://github.com/JuliaGraphs/Graphs.jl).
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- [Examples](https://github.com/JuliaGraphs/GraphNeuralNetworks.jl/tree/master/GraphNeuralNetworks/examples) of node, edge, and graph-level machine learning tasks.
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- Heterogeneous and dynamical graphs and convolutions.
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## Installation
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Install through the Julia package manager.
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```julia
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pkg> add GNNLux
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```
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## Usage
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For a comprehensive introduction to the library, refer to the [Documentation](https://juliagraphs.org/GraphNeuralNetworks.jl/docs/GNNLux.jl/).
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## Citing
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If you use GraphNeuralNetworks.jl in a scientific publication, we would appreciate a reference
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to [our paper](https://arxiv.org/abs/2412.06354):
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```
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@article{lucibello2024graphneuralnetworks,
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title={GraphNeuralNetworks.jl: Deep Learning on Graphs with Julia},
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author={Lucibello, Carlo and Rossi, Aurora},
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journal={arXiv preprint arXiv:2412.06354},
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url={https://arxiv.org/abs/2412.06354},
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year={2024}
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}
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```

‎GNNlib/README.md‎

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# GNNlib.jl
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This package contains a collection deep-learning framework agnostic
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building blocks for graph neural networks such as message passing operators and implementations of graph convolutional layers.
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This package contains a collection framework-agnostic
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building blocks for deep learning on graphs such as message passing operators and implementations of graph convolutional layers.
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In the future it will serve as the foundation of GraphNeuralNetworks.jl (based on Flux,jl).
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GNNlib.jl will be to GraphNeuralNetworks.jl what NNlib.jl is to Flux.jl and Lux.jl.
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This package is currently under development and may break frequentely.
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It is not meant for final users but for GNN libraries developers.
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Final user should use GraphNeuralNetworks.jl instead.
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See [GraphNeuralNetworks.jl](https://juliagraphs.org/GraphNeuralNetworks.jl/docs/GraphNeuralNetworks.jl/) for a Flux-based frontend package that uses this library and [GNNLux.jl](https://juliagraphs.org/GraphNeuralNetworks.jl/docs/GNNLux.jl/) for a Lux-based one.
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‎GraphNeuralNetworks/README.md‎

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<img align="right" width="300px" src="https://raw.githubusercontent.com/JuliaGraphs/GraphNeuralNetworks.jl/master/docs/src/assets/logo.svg">
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<img align="right" width="300px" src="https://raw.githubusercontent.com/JuliaGraphs/GraphNeuralNetworks.jl/master/docs/logo.svg">
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# GraphNeuralNetworks.jl
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[![](https://img.shields.io/badge/docs-stable-blue.svg)](https://JuliaGraphs.github.io/GraphNeuralNetworks.jl/stable)
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[![](https://img.shields.io/badge/docs-dev-blue.svg)](https://JuliaGraphs.github.io/GraphNeuralNetworks.jl/dev)
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![](https://github.com/JuliaGraphs/GraphNeuralNetworks.jl/actions/workflows/ci.yml/badge.svg)
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[![codecov](https://codecov.io/gh/JuliaGraphs/GraphNeuralNetworks.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/JuliaGraphs/GraphNeuralNetworks.jl)
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[![](https://img.shields.io/badge/docs-stable-blue.svg)](https://juliagraphs.org/GraphNeuralNetworks.jl/docs/GraphNeuralNetworks.jl/)
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Graph convolutional layers based on the deep learning framework [Flux.jl](https://fluxml.ai/).
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This is the frontend package for Flux users of the [GraphNeuralNetworks.jl ecosystem](https://github.com/JuliaGraphs/GraphNeuralNetworks.jl).
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GraphNeuralNetworks.jl is a graph neural network library written in Julia and based on the deep learning framework [Flux.jl](https://github.com/FluxML/Flux.jl).
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### Features
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Among its features:
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**GraphNeuralNetworks.jl** supports the following features:
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* Implements common graph convolutional layers.
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* Supports computations on batched graphs.
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* Easy to define custom layers.
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* CUDA support.
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* Integration with [Graphs.jl](https://github.com/JuliaGraphs/Graphs.jl).
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* [Examples](https://github.com/JuliaGraphs/GraphNeuralNetworks.jl/tree/master/examples) of node, edge, and graph level machine learning tasks.
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* Heterogeneous and temporal graphs.
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- Implementation of common graph convolutional layers.
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- Computation on batched graphs.
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- Custom layer definitions.
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- Support for CUDA and AMDGPU.
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- Integration with [Graphs.jl](https://github.com/JuliaGraphs/Graphs.jl).
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- [Examples](https://github.com/JuliaGraphs/GraphNeuralNetworks.jl/tree/master/GraphNeuralNetworks/examples) of node, edge, and graph-level machine learning tasks.
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- Heterogeneous and dynamical graphs and convolutions.
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## Installation
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## Installation
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GraphNeuralNetworks.jl is a registered Julia package. You can easily install it through the package manager:
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Install the package through the Julia package manager.
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```julia
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pkg> add GraphNeuralNetworks
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```
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## Usage
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Usage examples can be found in the [examples](https://github.com/JuliaGraphs/GraphNeuralNetworks.jl/tree/master/examples) and in the [notebooks](https://github.com/JuliaGraphs/GraphNeuralNetworks.jl/tree/master/notebooks) folder. Also, make sure to read the [documentation](https://JuliaGraphs.github.io/GraphNeuralNetworks.jl/dev) for a comprehensive introduction to the library.
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For a comprehensive introduction to the library, refer to the [Documentation](https://juliagraphs.org/GraphNeuralNetworks.jl/docs/GraphNeuralNetworks.jl/).
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## Citing
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If you use GraphNeuralNetworks.jl in a scientific publication, we would appreciate the following reference:
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If you use GraphNeuralNetworks.jl in a scientific publication, we would appreciate a reference
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to [our paper](https://arxiv.org/abs/2412.06354):
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```
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@misc{Lucibello2021GNN,
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author = {Carlo Lucibello and other contributors},
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title = {GraphNeuralNetworks.jl: a geometric deep learning library for the Julia programming language},
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year = 2021,
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url = {https://github.com/JuliaGraphs/GraphNeuralNetworks.jl}
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@article{lucibello2024graphneuralnetworks,
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title={GraphNeuralNetworks.jl: Deep Learning on Graphs with Julia},
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author={Lucibello, Carlo and Rossi, Aurora},
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journal={arXiv preprint arXiv:2412.06354},
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url={https://arxiv.org/abs/2412.06354},
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year={2024}
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}
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```
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## Acknowledgments
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GraphNeuralNetworks.jl is largely inspired by [PyTorch Geometric](https://pytorch-geometric.readthedocs.io/en/latest/), [Deep Graph Library](https://docs.dgl.ai/),
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and [GeometricFlux.jl](https://fluxml.ai/GeometricFlux.jl/stable/).
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```

‎README.md‎

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[![codecov](https://codecov.io/gh/JuliaGraphs/GraphNeuralNetworks.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/JuliaGraphs/GraphNeuralNetworks.jl)
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**Libraries for deep learning on graphs in Julia**, using either [Flux.jl](https://fluxml.ai/Flux.jl/stable/) or [Lux.jl](https://lux.csail.mit.edu/stable/) as backend frameworks.
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**Libraries for deep learning on graphs in Julia**, using either [Flux.jl](https://fluxml.ai/) or [Lux.jl](https://lux.csail.mit.edu/stable/) as backend frameworks.
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This repository contains the following packages:
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- **GraphNeuralNetworks.jl**: Provides graph convolutional layers based on the deep learning framework [Flux.jl](https://fluxml.ai/Flux.jl/stable/). This is the frontend package for Flux users.
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- **GraphNeuralNetworks.jl**: Provides graph convolutional layers based on the deep learning framework [Flux.jl](https://fluxml.ai/). This is the frontend package for Flux users.
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- **GNNLux.jl**: Offers graph convolutional layers based on the deep learning framework [Lux.jl](https://lux.csail.mit.edu/stable/). This is the frontend package for Lux users.
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- **GNNLux.jl**: Offers graph convolutional layers based on the deep learning framework [Lux.jl](https://lux.csail.mit.edu/). This is the frontend package for Lux users.
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- **GNNGraphs.jl**: Provides graph data structures and helper functions for working with graph data. This package is re-exported by the frontend packages.
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- Support for CUDA and AMDGPU.
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- Integration with [Graphs.jl](https://github.com/JuliaGraphs/Graphs.jl).
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- [Examples](https://github.com/JuliaGraphs/GraphNeuralNetworks.jl/tree/master/GraphNeuralNetworks/examples) of node, edge, and graph-level machine learning tasks.
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- Support for heterogeneous and temporal graphs.
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- Heterogeneous and dynamical graphs and convolutions.
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## Installation
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## Citing
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If you use GraphNeuralNetworks.jl in a scientific publication, we would appreciate the following reference:
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If you use GraphNeuralNetworks.jl in a scientific publication, we would appreciate a reference
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to [our paper](https://arxiv.org/abs/2412.06354):
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```
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@misc{Lucibello2021GNN,
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author = {Carlo Lucibello and other contributors},
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title = {GraphNeuralNetworks.jl: a geometric deep learning library for the Julia programming language},
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year = 2021,
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url = {https://github.com/JuliaGraphs/GraphNeuralNetworks.jl}
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@article{lucibello2024graphneuralnetworks,
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title={GraphNeuralNetworks.jl: Deep Learning on Graphs with Julia},
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author={Lucibello, Carlo and Rossi, Aurora},
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journal={arXiv preprint arXiv:2412.06354},
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url={https://arxiv.org/abs/2412.06354},
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year={2024}
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}
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```
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