LPLP: Learning Patterns for Link Prediction from Context

Link prediction is a fundamental task in graph machine learning with applications across diverse domains. It relies on structural patterns that vary substantially across graphs, making predictors learned on one graph difficult to transfer to another. Recent universal link predictors adapt to new graphs through labeled context, but still learn how to use this context from real graphs. We introduce **LPLP**, which **L**earns **P**atterns for **L**ink **P**rediction from context, following the prior-data fitted network paradigm. LPLP is trained entirely on synthetic graphs generated from a prior in which the structural patterns that predict links vary across tasks. Given an unseen graph with labeled context links, LPLP infers the structural pattern of the graph and scores query links without parameter updates. Across seven benchmarks, LPLP achieves the best average rank among methods evaluated on shared splits under both random and HeaRT hard negatives, improving average Hits@50 over the strongest baseline by 5.45 and 4.22 points, respectively. These results indicate that the ability to infer structural patterns from context can be learned from synthetic graph priors and applied to unseen graphs.