julia graphs

Author: CarloLucibello

GraphNeuralNetworks/README.md

@@ -1,12 +1,12 @@
-<img align="right" width="300px" src="https://raw.githubusercontent.com/CarloLucibello/GraphNeuralNetworks.jl/master/docs/src/assets/logo.svg">
+<img align="right" width="300px" src="https://raw.githubusercontent.com/JuliaGraphs/GraphNeuralNetworks.jl/master/docs/src/assets/logo.svg">
 
 
 # GraphNeuralNetworks.jl
 
-[![](https://img.shields.io/badge/docs-stable-blue.svg)](https://CarloLucibello.github.io/GraphNeuralNetworks.jl/stable)
-[![](https://img.shields.io/badge/docs-dev-blue.svg)](https://CarloLucibello.github.io/GraphNeuralNetworks.jl/dev)
-![](https://github.com/CarloLucibello/GraphNeuralNetworks.jl/actions/workflows/ci.yml/badge.svg)
-[![codecov](https://codecov.io/gh/CarloLucibello/GraphNeuralNetworks.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/CarloLucibello/GraphNeuralNetworks.jl)
+[![](https://img.shields.io/badge/docs-stable-blue.svg)](https://JuliaGraphs.github.io/GraphNeuralNetworks.jl/stable)
+[![](https://img.shields.io/badge/docs-dev-blue.svg)](https://JuliaGraphs.github.io/GraphNeuralNetworks.jl/dev)
+![](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)
 
 
 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).
@@ -18,7 +18,7 @@ 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. 
 * Heterogeneous and temporal graphs. 
 
 ## Installation
@@ -31,7 +31,7 @@ pkg> add GraphNeuralNetworks
 
 ## Usage
 
-Usage examples can be found in the [examples](https://github.com/CarloLucibello/GraphNeuralNetworks.jl/tree/master/examples) and in the [notebooks](https://github.com/CarloLucibello/GraphNeuralNetworks.jl/tree/master/notebooks) folder. Also, make sure to read the [documentation](https://CarloLucibello.github.io/GraphNeuralNetworks.jl/dev) for a comprehensive introduction to the library.
+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.
 
 
 ## Citing
@@ -43,7 +43,7 @@ If you use GraphNeuralNetworks.jl in a scientific publication, we would apprecia
   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}
+  url          = {https://github.com/JuliaGraphs/GraphNeuralNetworks.jl}
 }
 ```
 

GraphNeuralNetworks/docs/make.jl

@@ -55,4 +55,4 @@ makedocs(;
 
 tutorials_cb()
 
-deploydocs(repo = "github.com/CarloLucibello/GraphNeuralNetworks.jl.git")
+deploydocs(repo = "github.com/JuliaGraphs/GraphNeuralNetworks.jl.git")

GraphNeuralNetworks/docs/pluto_output/gnn_intro_pluto.md

@@ -29,7 +29,7 @@
     julia_version = "1.10.4"
 -->
 
-<div class="markdown"><p><em>This Pluto notebook is a Julia adaptation of the Pytorch Geometric tutorials that can be found <a href="https://pytorch-geometric.readthedocs.io/en/latest/notes/colabs.html">here</a>.</em></p><p>Recently, deep learning on graphs has emerged to one of the hottest research fields in the deep learning community. Here, <strong>Graph Neural Networks (GNNs)</strong> aim to generalize classical deep learning concepts to irregular structured data (in contrast to images or texts) and to enable neural networks to reason about objects and their relations.</p><p>This is done by following a simple <strong>neural message passing scheme</strong>, where node features <span class="tex">\(\mathbf{x}_i^{(\ell)}\)</span> of all nodes <span class="tex">\(i \in \mathcal{V}\)</span> in a graph <span class="tex">\(\mathcal{G} = (\mathcal{V}, \mathcal{E})\)</span> are iteratively updated by aggregating localized information from their neighbors <span class="tex">\(\mathcal{N}(i)\)</span>:</p><p class="tex">$$\mathbf{x}_i^{(\ell + 1)} = f^{(\ell + 1)}_{\theta} \left( \mathbf{x}_i^{(\ell)}, \left\{ \mathbf{x}_j^{(\ell)} : j \in \mathcal{N}(i) \right\} \right)$$</p><p>This tutorial will introduce you to some fundamental concepts regarding deep learning on graphs via Graph Neural Networks based on the <strong><a href="https://github.com/CarloLucibello/GraphNeuralNetworks.jl">GraphNeuralNetworks.jl library</a></strong>. GraphNeuralNetworks.jl is an extension library to the popular deep learning framework <a href="https://fluxml.ai/Flux.jl/stable/">Flux.jl</a>, and consists of various methods and utilities to ease the implementation of Graph Neural Networks.</p><p>Let's first import the packages we need:</p></div>
+<div class="markdown"><p><em>This Pluto notebook is a Julia adaptation of the Pytorch Geometric tutorials that can be found <a href="https://pytorch-geometric.readthedocs.io/en/latest/notes/colabs.html">here</a>.</em></p><p>Recently, deep learning on graphs has emerged to one of the hottest research fields in the deep learning community. Here, <strong>Graph Neural Networks (GNNs)</strong> aim to generalize classical deep learning concepts to irregular structured data (in contrast to images or texts) and to enable neural networks to reason about objects and their relations.</p><p>This is done by following a simple <strong>neural message passing scheme</strong>, where node features <span class="tex">\(\mathbf{x}_i^{(\ell)}\)</span> of all nodes <span class="tex">\(i \in \mathcal{V}\)</span> in a graph <span class="tex">\(\mathcal{G} = (\mathcal{V}, \mathcal{E})\)</span> are iteratively updated by aggregating localized information from their neighbors <span class="tex">\(\mathcal{N}(i)\)</span>:</p><p class="tex">$$\mathbf{x}_i^{(\ell + 1)} = f^{(\ell + 1)}_{\theta} \left( \mathbf{x}_i^{(\ell)}, \left\{ \mathbf{x}_j^{(\ell)} : j \in \mathcal{N}(i) \right\} \right)$$</p><p>This tutorial will introduce you to some fundamental concepts regarding deep learning on graphs via Graph Neural Networks based on the <strong><a href="https://github.com/JuliaGraphs/GraphNeuralNetworks.jl">GraphNeuralNetworks.jl library</a></strong>. GraphNeuralNetworks.jl is an extension library to the popular deep learning framework <a href="https://fluxml.ai/Flux.jl/stable/">Flux.jl</a>, and consists of various methods and utilities to ease the implementation of Graph Neural Networks.</p><p>Let's first import the packages we need:</p></div>
 
 <pre class='language-julia'><code class='language-julia'>begin
     using Flux

GraphNeuralNetworks/docs/src/datasets.md

@@ -1,6 +1,6 @@
 # Datasets
 
-GraphNeuralNetworks.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/).
+GraphNeuralNetworks.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/).
 
 GraphNeuralNetworks.jl provides the [`mldataset2gnngraph`](@ref) method for interfacing with MLDatasets.jl.
 

GraphNeuralNetworks/docs/src/dev.md

@@ -80,7 +80,7 @@ julia> compare(dfpr, dfmaster)
 
 Tutorials in GraphNeuralNetworks.jl are written in Pluto and rendered using [DemoCards.jl](https://github.com/JuliaDocs/DemoCards.jl) and [PlutoStaticHTML.jl](https://github.com/rikhuijzer/PlutoStaticHTML.jl). Rendering a Pluto notebook is time and resource-consuming, especially in a CI environment. So we use the [caching functionality](https://huijzer.xyz/PlutoStaticHTML.jl/dev/#Caching) provided by PlutoStaticHTML.jl to reduce CI time.
 
-If you are contributing a new tutorial or making changes to the existing notebook, generate the docs locally before committing/pushing. For caching to work, the cache environment(your local) and the documenter CI should have the same Julia version (e.g. "v1.9.1", also the patch number must match). So use the [documenter CI Julia version](https://github.com/CarloLucibello/GraphNeuralNetworks.jl/blob/master/.github/workflows/docs.yml#L17) for generating docs locally.
+If you are contributing a new tutorial or making changes to the existing notebook, generate the docs locally before committing/pushing. For caching to work, the cache environment(your local) and the documenter CI should have the same Julia version (e.g. "v1.9.1", also the patch number must match). So use the [documenter CI Julia version](https://github.com/JuliaGraphs/GraphNeuralNetworks.jl/blob/master/.github/workflows/docs.yml#L17) for generating docs locally.
 
 ```console
 julia --version # check julia version before generating docs
@@ -95,6 +95,6 @@ During the doc generation process, DemoCards.jl stores the cache notebooks in do
 git add docs/pluto_output # add generated cache
 ```
 
-Check the [documenter CI logs](https://github.com/CarloLucibello/GraphNeuralNetworks.jl/actions/workflows/docs.yml) to ensure that it used the local cache:
+Check the [documenter CI logs](https://github.com/JuliaGraphs/GraphNeuralNetworks.jl/actions/workflows/docs.yml) to ensure that it used the local cache:
 
 ![](https://user-images.githubusercontent.com/55111154/210061301-c84b7274-9e66-46fd-b272-d45b1c681d00.png)

GraphNeuralNetworks/docs/src/index.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
 

GraphNeuralNetworks/docs/src/messagepassing.md

@@ -134,7 +134,7 @@ function (l::GCN)(g::GNNGraph, x::AbstractMatrix{T}) where T
 end
 ```
 
-See the [`GATConv`](@ref) implementation [here](https://github.com/CarloLucibello/GraphNeuralNetworks.jl/blob/master/src/layers/conv.jl) for a more complex example.
+See the [`GATConv`](@ref) implementation [here](https://github.com/JuliaGraphs/GraphNeuralNetworks.jl/blob/master/src/layers/conv.jl) for a more complex example.
 
 
 ## Built-in message functions

GraphNeuralNetworks/docs/src/models.md

@@ -122,4 +122,4 @@ X = randn(Float32, din, 10)
 y = model(X) 
 ```
 
-An example of `WithGraph` usage is given in the graph neural ODE script in the [examples](https://github.com/CarloLucibello/GraphNeuralNetworks.jl/tree/master/examples) folder.
+An example of `WithGraph` usage is given in the graph neural ODE script in the [examples](https://github.com/JuliaGraphs/GraphNeuralNetworks.jl/tree/master/examples) folder.

GraphNeuralNetworks/docs/tutorials/index.md

@@ -9,7 +9,7 @@
 ## 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).
+[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

GraphNeuralNetworks/docs/tutorials/introductory_tutorials/gnn_intro_pluto.jl

@@ -37,7 +37,7 @@ This is done by following a simple **neural message passing scheme**, where node
 \mathbf{x}_i^{(\ell + 1)} = f^{(\ell + 1)}_{\theta} \left( \mathbf{x}_i^{(\ell)}, \left\{ \mathbf{x}_j^{(\ell)} : j \in \mathcal{N}(i) \right\} \right)
 ```
 
-This tutorial will introduce you to some fundamental concepts regarding deep learning on graphs via Graph Neural Networks based on the **[GraphNeuralNetworks.jl library](https://github.com/CarloLucibello/GraphNeuralNetworks.jl)**.
+This tutorial will introduce you to some fundamental concepts regarding deep learning on graphs via Graph Neural Networks based on the **[GraphNeuralNetworks.jl library](https://github.com/JuliaGraphs/GraphNeuralNetworks.jl)**.
 GraphNeuralNetworks.jl is an extension library to the popular deep learning framework [Flux.jl](https://fluxml.ai/Flux.jl/stable/), and consists of various methods and utilities to ease the implementation of Graph Neural Networks.
 
 Let's first import the packages we need: