What are the meanings of Node Classification, Link Prediction, Graph Classification in Graph Neural Network? I am currently studying Graph  Neural Network but I have some difficulty in understanding what I can do after having studied Graph Neural Network. From having gained a bit of understand in Graph Neural Network, I can do Node Classification, Link Prediction, and Graph Classification but I don't really understand what those 3 terms mean.
It is like I can study and understand how node embedding works with math but don't really understand where to use it. While studying this topic, all the contents explains where I can use it but those explanations are not really clear to me.
Hope somebody gives me clears examples of the three terms in my question.

Different problems that you can can tackle using a graph representation.
A graph is defined as a set of <V,E> (nodes , links).
Think of 2 examples.

*

*atoms and their bonds (making together a molecule).
in this case V= atoms, E= chmical bonds, graph is molecule


*users in social network being connected if they are friends (making together a social network.
in this case V= users, E= chmical bonds, graph is molecule
For Node classification given many valid molecules (training data)  and given a new incomplete data (molecule with unknown atoms comprising it ) ask the model to tell you with atom of the periodic table of chemical elements it is. This can be seen as a property of the node.
For link classification in the example 2 given example of users that are friends and not friends, ask the network if 2 users are friends or not.
For graph classification imagine now that you want to specify some molecules are safe to consume and not safe to consume by humans.
