Representation learning over temporal networks has drawn considerable attention in recent years. Efforts are mainly focused on modeling structural dependencies and temporal evolving regularities in Euclidean space which, however, underestimates the inherent complex and hierarchical properties in many real-world temporal networks, leading to sub-optimal embeddings. To explore these properties of a complex temporal network, we propose a hyperbolic temporal graph network (HTGN) that fully takes advantage of the exponential capacity and hierarchical awareness of hyperbolic geometry. More specially, HTGN maps the temporal graph into hyperbolic space, and incorporates hyperbolic graph neural network and hyperbolic gated recurrent neural network, to capture the evolving behaviors and implicitly preserve hierarchical information simultaneously. Furthermore, in the hyperbolic space, we propose two important modules that enable HTGN to successfully model temporal networks: (1) hyperbolic temporal contextual self-attention (HTA) module to attend to historical states and (2) hyperbolic temporal consistency (HTC) module to ensure stability and generalization. Experimental results on multiple real-world datasets demonstrate the superiority of HTGN for temporal graph embedding, as it consistently outperforms competing methods by significant margins in various temporal link prediction tasks. Specifically, HTGN achieves AUC improvement up to 9.98% for link prediction and 11.4% for new link prediction. Moreover, the ablation study further validates the representational ability of hyperbolic geometry and the effectiveness of the proposed HTA and HTC modules.

翻译：近些年来,在时间网络上进行的代表学习引起了相当的注意。主要的工作重点是模拟厄克利底空间的结构依赖性和时间变化规律性,但这种结构依赖性和时间变化规律性低估了许多现实世界时间网络内在的复杂和等级特性,导致出现亚最佳嵌入。为了探索复杂的时间网络的这些特性,我们提议了一个双曲时间图网络(HTGN)模块,充分利用超双曲几何的指数能力和等级意识。更具体地说,HTGN将时间图模块映射成超双曲空间,并纳入超双曲形神经网络和超双曲门门门的经常性神经网络,以同时捕捉许多现实世界时间网络中的变化行为和隐含的等级信息。此外,在超偏斜空间,我们提出了两个重要模块,使HTGN能够成功模拟时间网络的这些特性:(1) 超单曲时间背景自留(HTGN)模块,以关注历史状态;(2)超曲时序(HTC)模块,以确保稳定性和一般化。多个现实世界数据集的实验结果显示HGNGNG值网络的优越性和超链接的H98的超链接,以持续地平时标的HLLLLLLLLLLLLLLLLLLLLLLULLLLLLLLLLLLLL,以持续实现其重要日期预测。