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Co-authored-by: Carlo Lucibello <carlo.lucibello@gmail.com>

GNNLux/docs/src/tutorials/graph_classification.md

@@ -6,7 +6,7 @@ In this tutorial session we will have a closer look at how to apply **Graph Neur
 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**: