Graph neural networks (GNNs) find applications in various domains such as
computational biology, natural language processing, and computer security.
Owing to their popularity, there is an increasing need to explain GNN
predictions since GNNs are black-box machine learning models. One way to
address this is counterfactual reasoning where the objective is to change the
GNN prediction by minimal changes in the input graph. Existing methods for
counterfactual explanation of GNNs are limited to instance-specific local
reasoning. This approach has two major limitations of not being able to offer
global recourse policies and overloading human cognitive ability with too much
information. In this work, we study the global explainability of GNNs through
global counterfactual reasoning. Specifically, we want to find a small set of
representative counterfactual graphs that explains all input graphs. Towards
this goal, we propose GCFExplainer, a novel algorithm powered by
vertex-reinforced random walks on an edit map of graphs with a greedy summary.
Extensive experiments on real graph datasets show that the global explanation
from GCFExplainer provides important high-level insights of the model behavior
and achieves a 46.9% gain in recourse coverage and a 9.5% reduction in recourse
cost compared to the state-of-the-art local counterfactual explainers.

本研究提出了 GCFExplainer 算法来解释图神经网络的全局可解释性，通过全局反事实推理寻找一组代表性的反事实图形，实验结果表明与现有局部反事实解释器相比，GCFExplainer 算法能够提供更重要的高层次 Model 行为见解，并以 46.9% 的弥补覆盖率和 9.5% 的弥补成本降低取得更好的结果。