As for (1) I would agree. As for (2), that was a very specialist remark, not something to consider when starting a general exploratory (cluster) analysis.
A note about (1) however. If your data is already classified (each node comes with a label), then you can treat this classification as a clustering and see how well the data clustering matches the classification, using a criterion such as Variation of Information or split/join distance. This can be useful in a scenario where such a classification is available for one particular data set but not for others. It is then worthwhile to be aware that consistency is more important than exactness. That is, a data clustering can be a (near) super-clustering or sub-clustering of the classification and in that respect be consistent (see https://stats.stackexchange.com/questions/24961/comparing-clusterings-rand-index-vs-variation-of-information).