Update README.md

README.md

@@ -9,20 +9,20 @@
 
 Libraries for deep learning on graphs in Julia, using either [Flux.jl](https://fluxml.ai/Flux.jl/stable/) or [Lux.jl](https://lux.csail.mit.edu/stable/) as backend framework.
 
-This monorepo contains the following packages:
+This repository contains the following packages:
 
-- `GraphNeuralNetworks.jl`: Graph convolutional layers based on the deep learning framework [Flux.jl](https://fluxml.ai/Flux.jl/stable/). This is the fronted package for Flux users. 
+- **GraphNeuralNetworks.jl**: Graph convolutional layers based on the deep learning framework [Flux.jl](https://fluxml.ai/Flux.jl/stable/). This is the fronted package for Flux users. 
 
-- `GNNLux.jl`: Graph convolutional layers based on the deep learning framework [Lux.jl](https://lux.csail.mit.edu/stable/). This is the fronted package for Lux users. This package is still under development and it is not yet registered.
+- **GNNLux.jl**: Graph convolutional layers based on the deep learning framework [Lux.jl](https://lux.csail.mit.edu/stable/). This is the fronted package for Lux users. This package is still under development and not yet registered.
 
-- `GNNlib.jl`: Contains the message passing framework based on the gather/scatter mechanism or on
-  sparse matrix multiplication. It also contained the shared implementation for the layers of the two fronted packages. This package is not meant to be used directly by the user, but its functionalities
+- **GNNlib.jl**: Contains the message-passing framework based on the gather/scatter mechanism or on
+  sparse matrix multiplication. It also contains the shared implementation for the layers of the two fronted packages. This package is not meant to be used directly by the user, but its functionalities
   are used and re-exported by the fronted packages.
 
-- `GNNGraphs.jl`: Package that contains the graph data structures and helper functions for working with graph data. It depends on Graphs.jl package.
+- **GNNGraphs.jl**: Package that contains the graph data structures and helper functions for working with graph data.
 
 
-Both `GraphNeuralNetworks.jl` and `GNNLux.jl` enjoy several features:
+Both GraphNeuralNetworks.jl and GNNLux.jl enjoy several features:
 
 * Implement common graph convolutional layers.
 * Support computations on batched graphs. 
@@ -34,7 +34,7 @@ Both `GraphNeuralNetworks.jl` and `GNNLux.jl` enjoy several features:
 
 ## Installation
 
-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 :
+GraphNeuralNetworks.jl, GNNlib.jl and GNNGraphs.jl are a registered Julia packages. You can easily install a package, e.g. GraphNeuralNetworks.jl, through the package manager :
 
 ```julia
 pkg> add GraphNeuralNetworks
@@ -44,8 +44,7 @@ pkg> add GraphNeuralNetworks
 
 ## Usage
 
-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/).
-
+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/) and the [tutorials](https://juliagraphs.org/GraphNeuralNetworks.jl/tutorials/) for a comprehensive introduction to the library.
 
 ## Citing