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@@ -15,6 +15,49 @@ Here is a schema of the dependencies between the packages:
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Among its general features:
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* Implements common graph convolutional layers both in stateful and stateless form.
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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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## Installation
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GraphNeuralNetworks.jl, GNNlib.jl and GNNGraphs.jl are a registered Julia packages. You can easily install a package, for example GraphNeuralNetworks.jl, through the 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/GraphNeuralNetworks/examples) and in the [notebooks](https://github.com/JuliaGraphs/GraphNeuralNetworks.jl/tree/master/GraphNeuralNetworks/notebooks) folder. Also, make sure to read the [documentation](https://juliagraphs.org/GraphNeuralNetworks.jl/graphneuralnetworks/) for a comprehensive introduction to the library and the [tutorials](https://juliagraphs.org/GraphNeuralNetworks.jl/tutorials/).
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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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```
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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},
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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