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| # GNNGraphs.jl | ||
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| [](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. | ||
| It is not meant for final users but for GNN libraries developers. | ||
| Final user should use GraphNeuralNetworks.jl instead. | ||
| 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 | ||
| pkg> add GNNGraphs | ||
| ``` | ||
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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 | ||
| to [our paper](https://arxiv.org/abs/2412.06354): | ||
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| ``` | ||
| @article{lucibello2024graphneuralnetworks, | ||
| title={GraphNeuralNetworks.jl: Deep Learning on Graphs with Julia}, | ||
| author={Lucibello, Carlo and Rossi, Aurora}, | ||
| journal={arXiv preprint arXiv:2412.06354}, | ||
| url={https://arxiv.org/abs/2412.06354}, | ||
| year={2024} | ||
| } | ||
| ``` | ||
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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://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/). | ||
| 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. | ||
| - Computation on batched graphs. | ||
| - Custom layer definitions. | ||
| - Support for CUDA and AMDGPU. | ||
| - Integration with [Graphs.jl](https://github.com/JuliaGraphs/Graphs.jl). | ||
| - [Examples](https://github.com/JuliaGraphs/GraphNeuralNetworks.jl/tree/master/GraphNeuralNetworks/examples) of node, edge, and graph-level machine learning tasks. | ||
| - 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 | ||
| pkg> add GNNLux | ||
| ``` | ||
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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 | ||
| to [our paper](https://arxiv.org/abs/2412.06354): | ||
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| ``` | ||
| @article{lucibello2024graphneuralnetworks, | ||
| title={GraphNeuralNetworks.jl: Deep Learning on Graphs with Julia}, | ||
| author={Lucibello, Carlo and Rossi, Aurora}, | ||
| journal={arXiv preprint arXiv:2412.06354}, | ||
| url={https://arxiv.org/abs/2412.06354}, | ||
| year={2024} | ||
| } | ||
| ``` |
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| # GNNlib.jl | ||
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| This package contains a collection deep-learning framework agnostic | ||
| building blocks for graph neural networks such as message passing operators and implementations of graph convolutional layers. | ||
| This package contains a collection framework-agnostic | ||
| 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). | ||
| 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. | ||
| It is not meant for final users but for GNN libraries developers. | ||
| Final user should use GraphNeuralNetworks.jl instead. | ||
| 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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| <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://JuliaGraphs.github.io/GraphNeuralNetworks.jl/stable) | ||
| [](https://JuliaGraphs.github.io/GraphNeuralNetworks.jl/dev) | ||
|  | ||
| [](https://codecov.io/gh/JuliaGraphs/GraphNeuralNetworks.jl) | ||
| [](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/). | ||
| 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). | ||
| ### Features | ||
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| Among its features: | ||
| **GraphNeuralNetworks.jl** supports the following features: | ||
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| * Implements common graph convolutional layers. | ||
| * Supports computations on batched graphs. | ||
| * Easy to define custom layers. | ||
| * CUDA support. | ||
| * Integration with [Graphs.jl](https://github.com/JuliaGraphs/Graphs.jl). | ||
| * [Examples](https://github.com/JuliaGraphs/GraphNeuralNetworks.jl/tree/master/examples) of node, edge, and graph level machine learning tasks. | ||
| * Heterogeneous and temporal graphs. | ||
| - Implementation of common graph convolutional layers. | ||
| - Computation on batched graphs. | ||
| - Custom layer definitions. | ||
| - Support for CUDA and AMDGPU. | ||
| - Integration with [Graphs.jl](https://github.com/JuliaGraphs/Graphs.jl). | ||
| - [Examples](https://github.com/JuliaGraphs/GraphNeuralNetworks.jl/tree/master/GraphNeuralNetworks/examples) of node, edge, and graph-level machine learning tasks. | ||
| - Heterogeneous and dynamical graphs and convolutions. | ||
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| ## Installation | ||
| ## Installation | ||
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| GraphNeuralNetworks.jl is a registered Julia package. You can easily install it through the package manager: | ||
| Install the package through the Julia package manager. | ||
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| ```julia | ||
| pkg> add GraphNeuralNetworks | ||
| ``` | ||
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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: | ||
| If you use GraphNeuralNetworks.jl in a scientific publication, we would appreciate a reference | ||
| to [our paper](https://arxiv.org/abs/2412.06354): | ||
|
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| ``` | ||
| @misc{Lucibello2021GNN, | ||
| author = {Carlo Lucibello and other contributors}, | ||
| title = {GraphNeuralNetworks.jl: a geometric deep learning library for the Julia programming language}, | ||
| year = 2021, | ||
| url = {https://github.com/JuliaGraphs/GraphNeuralNetworks.jl} | ||
| @article{lucibello2024graphneuralnetworks, | ||
| title={GraphNeuralNetworks.jl: Deep Learning on Graphs with Julia}, | ||
| author={Lucibello, Carlo and Rossi, Aurora}, | ||
| journal={arXiv preprint arXiv:2412.06354}, | ||
| url={https://arxiv.org/abs/2412.06354}, | ||
| year={2024} | ||
| } | ||
| ``` | ||
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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/), | ||
| and [GeometricFlux.jl](https://fluxml.ai/GeometricFlux.jl/stable/). | ||
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| ``` |
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