Pathway-Aligned Supervision: Assigning Negative Samplers in Temporal Link Prediction

Temporal link predictors combine node context with pair history, yet their scoring pathways usually learn from the same negative examples. A comparison that distinguishes node representations can provide no direct gradient to an independently parameterized, deterministic history residual: identical history inputs cancel that residual in a pairwise score difference. This mismatch motivates Pathway-Aligned Supervision (PAS), which assigns random negatives to the node-score loss and historical negatives, with random fallback, to the full pair-score loss. With the scoring architecture fixed, we compare all four assignments and a validation-selected shared mixture. On tgbl-review, PAS raises the test mean reciprocal rank of a TGAT-based predictor from 0.20 under all-random supervision to 0.49 and exceeds the selected mixture; disjoint seeds confirm the gain. Better training contrast does not guarantee better ranking, however: PAS hurts tgbl-wiki, and changing evaluation candidates can reverse its advantage without retraining. Component experiments reveal a further distinction: a fitted history residual can help ranking even when changing its negative supervision harms the complete predictor. These findings support assigning negatives at the component level while evaluating the assignment under the intended ranking task.