History Doesn’t Repeat, but Its Patterns Echo: A Parallel Pairwise Negative-Sampling Framework for Temporal Link Prediction
TL;DR
This paper proposes ATNSF, a temporal graph learning framework with a hybrid negative sampling strategy that uses a portion of historical edges as hard negatives and introduces a pairwise sampled softmax loss that contrasts each positive instance with a batch of negatives to learn more discriminative representations.
Abstract
Temporal link prediction with temporal graph neural networks (TGNNs) is increasingly used to model spatio-temporal dependencies in temporal graphs and to forecast future interactions among entities. Existing sampling-based training methods typically rely on random negative sampling and pointwise loss formulations, which often lead to suboptimal convergence and limited generalization due to low-quality negative samples. We propose ATNSF, a temporal graph learning framework with a hybrid negative sampling strategy that uses a portion of historical edges as hard negatives. For efficiency, we design an asynchronous parallel training pipeline for scalable optimization and introduce a pairwise sampled softmax loss that contrasts each positive instance with a batch of negatives to learn more discriminative representations. Finally, we theoretically show that jointly designing the loss function and negative sampling strategy is crucial for improving performance and generalization. Extensive experiments across six temporal graph datasets demonstrate that ATNSF improves the average AP from 0.724 to 0.827 (+0.103). Remarkably, it also accelerates training by 1.37× to 6.85×, achieving a 2.57× geometric mean speedup. The source code of this paper can be found at https://github.com/yongqiu-star/ATNSF.