Temporal graph neural networks Tgnn have exhibited state-of-art performance in future-link prediction tasks. Training of these TGNNs is enumerated by uniform random sampling based unsupervised loss. During training, in the context of a positive example, the loss is computed over uninformative negatives, which introduces redundancy and sub-optimal performance. In this paper, we propose modified unsupervised learning of Tgnn, by replacing the uniform negative sampling with importance-based negative sampling. We theoretically motivate and define the dynamically computed distribution for a sampling of negative examples. Finally, using empirical evaluations over three real-world datasets, we show that Tgnn trained using loss based on proposed negative sampling provides consistent superior performance.

翻译：时序图神经网络（TGNN）在未来的链接预测任务中展现了最先进的性能。这些TGNN的训练过程通过基于均匀随机采样的无监督损失来实现。在训练过程中，针对正例样本，损失计算会涉及非信息性负样本，从而引入冗余并导致次优性能。本文提出对TGNN的无监督学习进行改进，将均匀负采样替换为基于重要性的负采样。我们从理论上论证并定义了用于负样本采样的动态计算分布。最后，通过对三个真实数据集的实证评估表明，采用基于所提负采样的损失训练的TGNN能够持续提供更优性能。