Rethinking Link Prediction Evaluation: Full-Space Reference and Model-Screened Candidates

Link prediction aims to infer unobserved edges in graphs and underlies many real-world applications. In practice, industrial systems typically use learned screening models to narrow vast candidate spaces and retain high-likelihood candidates before downstream prediction. However, academic evaluation protocols often construct candidate sets by randomly sampling or heuristically selecting non-edges, which may omit plausible and challenging negatives and lead to overly optimistic estimates of predictive capability. To quantify the effect of such candidate omission without imposing an additional selection rule, we use full-space evaluation, which ranks each test edge against the complete valid candidate space, as an idealized selection-free reference. Across 20 methods and seven datasets, we find that heuristic candidate selection can substantially inflate measured performance and distort method comparisons. Because full-space evaluation becomes costly on large graphs, we propose model-screened evaluation, which adapts the learned screening principle used in industrial systems to academic benchmarking and uses a learned model to retain a controllable number of high-likelihood candidates. With a fixed screening architecture and training configuration across all datasets, our protocol closely preserves the evaluation conclusions of the full-space reference. For example, on ogbl-ddi, model-screened evaluation reduces the average MRR deviation from full-space evaluation from 14.8 to 0.16 percentage points and the average pairwise MRR-gap deviation from 4.5 to 0.08.