Transferring knowledge across graphs plays a pivotal role in many high-stake
domains, ranging from transportation networks to e-commerce networks, from
neuroscience to finance. To date, the vast majority of existing works assume
both source and target domains are sampled from a universal and stationary
distribution. However, many real-world systems are intrinsically dynamic, where
the underlying domains are evolving over time. To bridge the gap, we propose to
shift the problem to the dynamic setting and ask: given the label-rich source
graphs and the label-scarce target graphs observed in previous T timestamps,
how can we effectively characterize the evolving domain discrepancy and
optimize the generalization performance of the target domain at the incoming
T+1 timestamp? To answer the question, for the first time, we propose a
generalization bound under the setting of dynamic transfer learning across
graphs, which implies the generalization performance is dominated by domain
evolution and domain discrepancy between source and target domains. Inspired by
the theoretical results, we propose a novel generic framework DyTrans to
improve knowledge transferability across dynamic graphs. In particular, we
start with a transformer-based temporal encoding module to model temporal
information of the evolving domains; then, we further design a dynamic domain
unification module to efficiently learn domain-invariant representations across
the source and target domains. Finally, extensive experiments on various
real-world datasets demonstrate the effectiveness of DyTrans in transferring
knowledge from dynamic source domains to dynamic target domains.

该研究提出了一个动态传输学习框架 DyTrans，使用基于 transformer 的时间编码模块建模动态域的时间信息，设计了动态域统一模块跨源和目标域高效地学习域不变特征，并在多个实际数据集上展示了 DyTrans 在从动态源域到动态目标域的知识转移中的有效性。