Merge branch 'lux-tutorial-graphclassification' of https://github.com…

…/JuliaGraphs/GraphNeuralNetworks.jl into lux-tutorial-graphclassification

GNNLux/docs/src/tutorials/graph_classification.md

@@ -3,10 +3,10 @@
 *This tutorial is a Julia adaptation of the Pytorch Geometric tutorial that can be found [here](https://pytorch-geometric.readthedocs.io/en/latest/notes/colabs.html).*
 
 In this tutorial session we will have a closer look at how to apply **Graph Neural Networks (GNNs) to the task of graph classification**.
-Graph classification refers to the problem of classifying entire graphs (in contrast to nodes), given a **dataset of graphs**, based on some structural graph properties.
+Graph classification refers to the problem of classifying entire graphs (in contrast to nodes), given a **dataset of graphs**, based on some structural graph properties and possibly on some input node features.
 Here, we want to embed entire graphs, and we want to embed those graphs in such a way so that they are linearly separable given a task at hand.
 
-The most common task for graph classification is **molecular property prediction**, in which molecules are represented as graphs, and the task may be to infer whether a molecule inhibits HIV virus replication or not.
+A common graph classification task is **molecular property prediction**, in which molecules are represented as graphs, and the task may be to infer whether a molecule inhibits HIV virus replication or not.
 
 The TU Dortmund University has collected a wide range of different graph classification datasets, known as the [**TUDatasets**](https://chrsmrrs.github.io/datasets/), which are also accessible via MLDatasets.jl.
 Let's import the necessary packages. Then we'll load and inspect one of the smaller ones, the **MUTAG dataset**: