I was reading about different graph machine learning tasks in this book (Chapter 1) here and to learn about node classification and graph classification tasks.
Then I looked at this paper here, which is about semi-supervised classification for node classification.
Question: Why is/can node classification (graph machine learning) be semi-supervised while graph classification is just supervised?
Attempted explanation: Is it because:
- It would be harder to chop off parts of the graphs for node classification in order to keep the testing nodes unobservable. On the contrary, graph classification is a process for which it is very easy to ignore the testing ’data’ (i.e. graphs) as they are completely separate from our training graphs; they are not physically connected
- Secondly, there is information to be gained from the testing nodes as is mentioned above - for example, the neighborhood of the node
Any help is appreciated. Thanks