diff --git a/README.md b/README.md
index ceb716a93..1cb508a46 100644
--- a/README.md
+++ b/README.md
@@ -1,12 +1,12 @@
-<img align="right" width="300px" src="https://raw.githubusercontent.com/GraphNeuralNetworks/GraphNeuralNetworks.jl/master/GraphNeuralNetworks/docs/src/assets/logo.svg">
+<img align="right" width="300px" src="https://raw.githubusercontent.com/JuliaGraphs/GraphNeuralNetworks.jl/master/GraphNeuralNetworks/docs/src/assets/logo.svg">
 
 
 # GraphNeuralNetworks.jl
 
-[![](https://img.shields.io/badge/docs-stable-blue.svg)](https://GraphNeuralNetworks.github.io/GraphNeuralNetworks.jl/stable)
-[![](https://img.shields.io/badge/docs-dev-blue.svg)](https://GraphNeuralNetworks.github.io/GraphNeuralNetworks.jl/dev)
-![](https://github.com/GraphNeuralNetworks/GraphNeuralNetworks.jl/actions/workflows/ci.yml/badge.svg)
-[![codecov](https://codecov.io/gh/GraphNeuralNetworks/GraphNeuralNetworks.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/GraphNeuralNetworks/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/GraphNeuralNetworks/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/GraphNeuralNetworks/GraphNeuralNetworks.jl/tree/master/examples) and in the [notebooks](https://github.com/GraphNeuralNetworks/GraphNeuralNetworks.jl/tree/master/notebooks) folder. Also, make sure to read the [documentation](https://GraphNeuralNetworks.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/GraphNeuralNetworks/GraphNeuralNetworks.jl}
+  url          = {https://github.com/JuliaGraphs/GraphNeuralNetworks.jl}
 }
 ```