The strongest scientific view treats missing-link inference as probabilistic model selection, not graph clairvoyance. Candidate edges can be ranked using topology, node attributes, latent representations, or graph neural networks. The key safeguard is temporal or held-out evaluation: hide links whose existence is known, then measure recovery. A high score means the model captures repeatable structure in that dataset; it does not establish that every highly ranked absent edge exists in the real world.