  Prediction using the ground truth sounds like an oxymoron in machine
learning. However, such an unrealistic setting was used in hundreds, if not
thousands of papers in the area of finding graph representations. To evaluate
the multi-label problem of node classification by using the obtained
representations, many works assume in the prediction stage that the number of
labels of each test instance is known. In practice such ground truth
information is rarely available, but we point out that such an inappropriate
setting is now ubiquitous in this research area. We detailedly investigate why
the situation occurs. Our analysis indicates that with unrealistic information,
the performance is likely over-estimated. To see why suitable predictions were
not used, we identify difficulties in applying some multi-label techniques. For
the use in future studies, we propose simple and effective settings without
using practically unknown information. Finally, we take this chance to conduct
a fair and serious comparison of major graph-representation learning methods on
multi-label node classification.
