How should we understand the evaluation metric, AUC, in link prediction problems?

In link prediction problems, there are only known edges and nodes.

• If there is a known edge in the node pair, the node pair is regarded as a positive sample. Except for those node pairs whose edges are known, There may exist unobserved edges in some node pairs or there really doesn't exist edges in some node pairs. Our target is to predict potential links in those candidate node pairs.

The node pair where there exist known edge is regarded as a positive sample. So the node pair whose edge are not observed can neither be regarded as a positive example, nor a negative example.

So I think link prediction problem is a semi-supervised problem. However, I find that many papers, for example, GRTR: Drug-Disease Association Prediction Based on Graph Regularized Transductive Regression on Heterogeneous Network, use AUC(Area Under the ROC Curve, a metric for supervised problems) as the metric.

How should we understand such behavior? What's the reason?

• you should probably cite a specific paper showing an example of the specific use-case your interested in – mshlis Jul 12 at 1:19
• – Ben Jul 12 at 1:47