  Non-IID transfer learning on graphs is crucial in many high-stakes domains.
The majority of existing works assume stationary distribution for both source
and target domains. However, real-world graphs are intrinsically dynamic,
presenting challenges in terms of domain evolution and dynamic discrepancy
between source and target domains. To bridge the gap, we shift the problem to
the dynamic setting and pose the question: given the label-rich source graphs
and the label-scarce target graphs both 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 it, we propose a generalization bound for dynamic non-IID
transfer learning on graphs, which implies the generalization performance is
dominated by domain evolution and domain discrepancy between source and target
graphs. Inspired by the theoretical results, we introduce a novel generic
framework named EvoluNet. It leverages a transformer-based temporal encoding
module to model temporal information of the evolving domains and then uses a
dynamic domain unification module to efficiently learn domain-invariant
representations across the source and target domains. Finally, EvoluNet
outperforms the state-of-the-art models by up to 12.1%, demonstrating its
effectiveness in transferring knowledge from dynamic source graphs to dynamic
target graphs.
