https://github.com/axsk/graphneuralnetworks.jl

Graph Neural Networks in Julia

https://github.com/axsk/graphneuralnetworks.jl

Repository

Graph Neural Networks in Julia

https://github.com/axsk/GraphNeuralNetworks.jl/blob/master/

# GraphNeuralNetworks.jl

[![](https://img.shields.io/badge/docs-Flux-blue.svg)](https://juliagraphs.org/GraphNeuralNetworks.jl/docs/GraphNeuralNetworks.jl/)
[![](https://img.shields.io/badge/docs-Lux-blue.svg)](https://juliagraphs.org/GraphNeuralNetworks.jl/docs/GNNLux.jl/)
![](https://github.com/JuliaGraphs/GraphNeuralNetworks.jl/actions/workflows/ci.yml/badge.svg)
[![codecov](https://codecov.io/gh/JuliaGraphs/GraphNeuralNetworks.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/JuliaGraphs/GraphNeuralNetworks.jl)


**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.

This repository contains the following packages:

- **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.

- **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.

- **GNNGraphs.jl**: Provides graph data structures and helper functions for working with graph data. This package is re-exported by the frontend packages.

- **GNNlib.jl**: Implements the message-passing framework based on the gather/scatter mechanism or sparse matrix multiplication. It also includes shared implementations for the layers used by the two frontend packages. This package is not intended for direct use by end-users but is re-exported by the frontend packages.

### Features

Both **GraphNeuralNetworks.jl** and **GNNLux.jl** support the following features:

- 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.

## Installation  

All packages are registered in the General registry, making them easy to install via the Julia package manager.

For **Flux** users, run:
```julia
pkg> add GraphNeuralNetworks
```

For **Lux** users, run:
```julia
pkg> add GNNLux
```

There is no need to install GNNGraphs or GNNlib directly, as their functionality is re-exported by the frontend packages.

## Usage

Usage examples can be found in the [examples folder](https://github.com/JuliaGraphs/GraphNeuralNetworks.jl/tree/master/GraphNeuralNetworks/examples) and the [notebooks folder](https://github.com/JuliaGraphs/GraphNeuralNetworks.jl/tree/master/GraphNeuralNetworks/notebooks). 

For a comprehensive introduction to the library, refer to the [Documentation](https://juliagraphs.org/GraphNeuralNetworks.jl/).

## Citing

If you use GraphNeuralNetworks.jl in a scientific publication, we would appreciate a reference
to [our paper](https://arxiv.org/abs/2412.06354):

```
@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}
}
```

## Acknowledgments

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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