This study focuses on long-term forecasting (LTF) on continuous-time dynamic
graph networks (CTDGNs), which is important for real-world modeling. Existing
CTDGNs are effective for modeling temporal graph data due to their ability to
capture complex temporal dependencies but perform poorly on LTF due to the
substantial requirement for historical data, which is not practical in most
cases. To relieve this problem, a most intuitive way is data augmentation. In
this study, we propose \textbf{\underline{U}ncertainty \underline{M}asked
\underline{M}ix\underline{U}p (UmmU)}: a plug-and-play module that conducts
uncertainty estimation to introduce uncertainty into the embedding of
intermediate layer of CTDGNs, and perform masked mixup to further enhance the
uncertainty of the embedding to make it generalize to more situations. UmmU can
be easily inserted into arbitrary CTDGNs without increasing the number of
parameters. We conduct comprehensive experiments on three real-world dynamic
graph datasets, the results demonstrate that UmmU can effectively improve the
long-term forecasting performance for CTDGNs.

该研究提出了一种基于数据增强的 plug-and-play 模块 UmmU，采用不确定性估计和遮盖式 Mixup 技术来增加嵌入层的不确定性，进一步增强嵌入层的泛化能力，结果表明 UmmU 可以有效提高连续时间动态图网络的长期预测性能。