  Counterfactual explanations of Graph Neural Networks (GNNs) offer a powerful
way to understand data that can naturally be represented by a graph structure.
Furthermore, in many domains, it is highly desirable to derive data-driven
global explanations or rules that can better explain the high-level properties
of the models and data in question. However, evaluating global counterfactual
explanations is hard in real-world datasets due to a lack of human-annotated
ground truth, which limits their use in areas like molecular sciences.
Additionally, the increasing scale of these datasets provides a challenge for
random search-based methods. In this paper, we develop a novel global
explanation model RLHEX for molecular property prediction. It aligns the
counterfactual explanations with human-defined principles, making the
explanations more interpretable and easy for experts to evaluate. RLHEX
includes a VAE-based graph generator to generate global explanations and an
adapter to adjust the latent representation space to human-defined principles.
Optimized by Proximal Policy Optimization (PPO), the global explanations
produced by RLHEX cover 4.12% more input graphs and reduce the distance between
the counterfactual explanation set and the input set by 0.47% on average across
three molecular datasets. RLHEX provides a flexible framework to incorporate
different human-designed principles into the counterfactual explanation
generation process, aligning these explanations with domain expertise. The code
and data are released at https://github.com/dqwang122/RLHEX.
