Link prediction estimates which unobserved pairs of nodes are likely to be connected.

In a graph G(V,E), the observed edges form only part of the possible network. Link prediction evaluates absent node pairs and assigns them a likelihood of being real or of appearing later. A common test hides a subset of known edges, trains on the remainder, and checks whether the method retrieves the withheld links. This turns an apparently unknowable problem into supervised validation, while preserving the distinction between missing data and genuinely nonexistent relationships.