  This paper focuses on out-of-distribution generalization on graphs where
performance drops due to the unseen distribution shift. Previous graph domain
generalization works always resort to learning an invariant predictor among
different source domains. However, they assume sufficient source domains are
available during training, posing huge challenges for realistic applications.
By contrast, we propose a new graph domain generalization framework, dubbed as
DPS, by constructing multiple populations from the source domains.
Specifically, DPS aims to discover multiple \textbf{D}iverse and
\textbf{P}redictable \textbf{S}ubgraphs with a set of generators, namely,
subgraphs are different from each other but all the them share the same
semantics with the input graph. These generated source domains are exploited to
learn an \textit{equi-predictive} graph neural network (GNN) across domains,
which is expected to generalize well to unseen target domains. Generally, DPS
is model-agnostic that can be incorporated with various GNN backbones.
Extensive experiments on both node-level and graph-level benchmarks shows that
the proposed DPS achieves impressive performance for various graph domain
generalization tasks.
