Change repo

Author: aurorarossi

GNNGraphs/docs/make.jl

@@ -29,5 +29,5 @@ makedocs(;
 
 
 
-deploydocs(;repo = "https://github.com/CarloLucibello/GraphNeuralNetworks.jl.git",
+deploydocs(;repo = "https://github.com/JuliaGraphs/GraphNeuralNetworks.jl.git",
 dirname = "GNNGraphs")

GNNGraphs/docs/src/datasets.md

@@ -1,6 +1,6 @@
 # Datasets
 
-GNNGraphs.jl doesn't come with its own datasets, but leverages those available in the Julia (and non-Julia) ecosystem. In particular, the [examples in the GraphNeuralNetworks.jl repository](https://github.com/CarloLucibello/GraphNeuralNetworks.jl/tree/master/examples) make use of the [MLDatasets.jl](https://github.com/JuliaML/MLDatasets.jl) package. There you will find common graph datasets such as Cora, PubMed, Citeseer, TUDataset and [many others](https://juliaml.github.io/MLDatasets.jl/dev/datasets/graphs/).
+GNNGraphs.jl doesn't come with its own datasets, but leverages those available in the Julia (and non-Julia) ecosystem. In particular, the [examples in the GraphNeuralNetworks.jl repository](https://github.com/JuliaGraphs/GraphNeuralNetworks.jl/tree/master/examples) make use of the [MLDatasets.jl](https://github.com/JuliaML/MLDatasets.jl) package. There you will find common graph datasets such as Cora, PubMed, Citeseer, TUDataset and [many others](https://juliaml.github.io/MLDatasets.jl/dev/datasets/graphs/).
 For graphs with static structures and temporal features, datasets such as METRLA, PEMSBAY, ChickenPox, and WindMillEnergy are available. For graphs featuring both temporal structures and temporal features, the TemporalBrains dataset is suitable.
 
 GraphNeuralNetworks.jl provides the [`mldataset2gnngraph`](@ref) method for interfacing with MLDatasets.jl.

GNNLux/docs/make.jl

@@ -22,5 +22,5 @@ makedocs(;
 
 
 
-deploydocs(;repo = "https://github.com/CarloLucibello/GraphNeuralNetworks.jl.git",
+deploydocs(;repo = "https://github.com/JuliaGraphs/GraphNeuralNetworks.jl.git",
 dirname = "GNNLux")

GNNlib/docs/make.jl

@@ -36,5 +36,5 @@ makedocs(;
 
 
 
-deploydocs(;repo = "https://github.com/CarloLucibello/GraphNeuralNetworks.jl.git",
+deploydocs(;repo = "https://github.com/JuliaGraphs/GraphNeuralNetworks.jl.git",
 dirname = "GNNlib")

GraphNeuralNetworks/docs/make.jl

@@ -48,4 +48,4 @@ makedocs(;
 
 
 
-deploydocs(;repo = "https://github.com/CarloLucibello/GraphNeuralNetworks.jl.git", dirname= "GraphNeuralNetworks")
+deploydocs(;repo = "https://github.com/JuliaGraphs/GraphNeuralNetworks.jl.git", dirname= "GraphNeuralNetworks")

GraphNeuralNetworks/docs/src/home.md

@@ -1,6 +1,6 @@
 # GraphNeuralNetworks
 
-This is the documentation page for [GraphNeuralNetworks.jl](https://github.com/CarloLucibello/GraphNeuralNetworks.jl), a graph neural network library written in Julia and based on the deep learning framework [Flux.jl](https://github.com/FluxML/Flux.jl).
+This is the documentation page for [GraphNeuralNetworks.jl](https://github.com/JuliaGraphs/GraphNeuralNetworks.jl), a graph neural network library written in Julia and based on the deep learning framework [Flux.jl](https://github.com/FluxML/Flux.jl).
 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/).
 
@@ -11,15 +11,15 @@ Among its features:
 * Easy to define custom layers.
 * CUDA support.
 * Integration with [Graphs.jl](https://github.com/JuliaGraphs/Graphs.jl).
-* [Examples](https://github.com/CarloLucibello/GraphNeuralNetworks.jl/tree/master/examples) of node, edge, and graph level machine learning tasks. 
+* [Examples](https://github.com/JuliaGraphs/GraphNeuralNetworks.jl/tree/master/examples) of node, edge, and graph level machine learning tasks. 
 
 
 ## Package overview
 
 Let's give a brief overview of the package by solving a  
 graph regression problem with synthetic data. 
 
-Usage examples on real datasets can be found in the [examples](https://github.com/CarloLucibello/GraphNeuralNetworks.jl/tree/master/examples) folder. 
+Usage examples on real datasets can be found in the [examples](https://github.com/JuliaGraphs/GraphNeuralNetworks.jl/tree/master/examples) folder. 
 
 ### Data preparation
 

tutorials/docs/make.jl

@@ -30,5 +30,5 @@ makedocs(;
 
 
 
-deploydocs(; repo = "https://github.com/CarloLucibello/GraphNeuralNetworks.jl.git",
+deploydocs(; repo = "https://github.com/JuliaGraphs/GraphNeuralNetworks.jl.git",
     dirname = "tutorials")

tutorials/docs/src/index.md

@@ -21,4 +21,4 @@ Here some tutorials on temporal graph neural networks:
 
 ## Contributions
 
-If you have a suggestion on adding new tutorials, feel free to create a new issue [here](https://github.com/CarloLucibello/GraphNeuralNetworks.jl/issues/new). Users are invited to contribute demonstrations of their own. If you want to contribute new tutorials and looking for inspiration, checkout these tutorials from [PyTorch Geometric](https://pytorch-geometric.readthedocs.io/en/latest/notes/colabs.html). Please check out existing tutorials for more details.
+If you have a suggestion on adding new tutorials, feel free to create a new issue [here](https://github.com/JuliaGraphs/GraphNeuralNetworks.jl/issues/new). Users are invited to contribute demonstrations of their own. If you want to contribute new tutorials and looking for inspiration, checkout these tutorials from [PyTorch Geometric](https://pytorch-geometric.readthedocs.io/en/latest/notes/colabs.html). Please check out existing tutorials for more details.