Improving out-of-distribution generalization in graphs via hierarchical
  semantic environments
Yinhua Piao Sangseon Lee Yijingxiu Lu Sun Kim
Out-of-distribution (OOD) generalization in the graph domain is challenging
due to complex distribution shifts and a lack of environmental contexts. Recent
methods attempt to enhance graph OOD generalization by generating flat
environments. However, such flat environments come with inherent limitations to
capture more complex data distributions. Considering the DrugOOD dataset, which
contains diverse training environments (e.g., scaffold, size, etc.), flat
contexts cannot sufficiently address its high heterogeneity. Thus, a new
challenge is posed to generate more semantically enriched environments to
enhance graph invariant learning for handling distribution shifts. In this
paper, we propose a novel approach to generate hierarchical semantic
environments for each graph. Firstly, given an input graph, we explicitly
extract variant subgraphs from the input graph to generate proxy predictions on
local environments. Then, stochastic attention mechanisms are employed to
re-extract the subgraphs for regenerating global environments in a hierarchical
manner. In addition, we introduce a new learning objective that guides our
model to learn the diversity of environments within the same hierarchy while
maintaining consistency across different hierarchies. This approach enables our
model to consider the relationships between environments and facilitates robust
graph invariant learning. Extensive experiments on real-world graph data have
demonstrated the effectiveness of our framework. Particularly, in the
challenging dataset DrugOOD, our method achieves up to 1.29\% and 2.83\%
improvement over the best baselines on IC50 and EC50 prediction tasks,
respectively.