Temporal graph neural network has recently received significant attention due to its wide application scenarios, such as bioinformatics, knowledge graphs, and social networks. There are some temporal graph neural networks that achieve remarkable results. However, these works focus on future event prediction and are performed under the assumption that all historical events are observable. In real-world applications, events are not always observable, and estimating event time is as important as predicting future events. In this paper, we propose MTGN, a missing event-aware temporal graph neural network, which uniformly models evolving graph structure and timing of events to support predicting what will happen in the future and when it will happen.MTGN models the dynamic of both observed and missing events as two coupled temporal point processes, thereby incorporating the effects of missing events into the network. Experimental results on several real-world temporal graphs demonstrate that MTGN significantly outperforms existing methods with up to 89% and 112% more accurate time and link prediction. Code can be found on https://github.com/HIT-ICES/TNNLS-MTGN.
翻译:时间图神经网络最近因其广泛的应用情景,例如生物信息学、知识图和社交网络等,受到极大关注。有些时间图神经网络取得了显著的成果。然而,这些工程侧重于未来事件预测,并假设所有历史事件都可观测到。在现实世界的应用中,事件并非总能观测,估计事件时间与预测未来事件同样重要。在本文中,我们提议MTGN,即一个缺少的事件识别时间图神经网络,统一地模拟事件演变的图表结构和时间,以支持预测未来和将来会发生什么情况。MTGN将所观察到和失踪事件的动态作为两个结合的时间点过程,从而将失踪事件的影响纳入网络。几个现实世界时间图的实验结果显示,MTGN大大超过现有方法,达到89%和112%的准确时间和链接预测。代码见https://github.com/HIT-ICES/TNNLS-MTGN。