Do Temporal Link Predictors Need Learned Memory? A Smoothed-Count Baseline with a Handful of Parameters

The paper investigates whether temporal link predictors can forgo learned node representations in favor of simple statistical counts of interaction patterns. It introduces a predictor that aggregates transition and co‑occurrence counts, smooths them with destination frequencies or Kneser‑Ney continuation counts, and combines these with popularity, source history, and recency via a shared log‑linear rule. With only 9–13 learned parameters, the model achieves the best mean reciprocal rank on 7 of 16 datasets and outperforms several baselines across all evaluated datasets, demonstrating that a lightweight, count‑based approach can rival more complex neural methods.