GraphNeuralNetworks.jl is a graph neural network library written in Julia and based on the deep learning framework Flux.jl.
Among its features:
- Implements common graph convolutional layers.
- Supports computations on batched graphs.
- Easy to define custom layers.
- CUDA support.
- Integration with Graphs.jl.
- Examples of node, edge, and graph level machine learning tasks.
- Heterogeneous and temporal graphs.
GraphNeuralNetworks.jl is a registered Julia package. You can easily install it through the package manager:
pkg> add GraphNeuralNetworks
Usage examples can be found in the examples and in the notebooks folder. Also, make sure to read the documentation for a comprehensive introduction to the library.
If you use GraphNeuralNetworks.jl in a scientific publication, we would appreciate the following reference:
@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/CarloLucibello/GraphNeuralNetworks.jl}
}
GraphNeuralNetworks.jl is largely inspired by PyTorch Geometric, Deep Graph Library, and GeometricFlux.jl.