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Prediction using the ground truth sounds like an oxymoron in machinelearning. However, such an unrealistic setting was used in hundreds, if notthousands of papers in the area of finding graph representations. To evaluatethe multi-label problem of node classification by using the obtainedrepresentations, many works assume in the prediction stage that the number oflabels of each test instance is known. In practice such ground truthinformation is rarely available, but we point out that such an inappropriatesetting is now ubiquitous in this research area. We detailedly investigate whythe situation occurs. Our analysis indicates that with unrealistic information,the performance is likely over-estimated. To see why suitable predictions werenot used, we identify difficulties in applying some multi-label techniques. Forthe use in future studies, we propose simple and effective settings withoutusing practically unknown information. Finally, we take this chance to conducta fair and serious comparison of major graph-representation learning methods onmulti-label node classification.

On the Use of Unrealistic Predictions in Hundreds of Papers Evaluating Graph Representations