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.

本文研究机器学习中关于多标签节点分类问题的性能评估方法，发现许多相关研究采用不切实际的标签真值进行预测，导致结果被高估；本文提出了一种简单有效的评估方法，并在此基础上，对主要的图表示学习方法进行了公正深入的比较。