Generating Robust Counterfactual Witnesses for Graph Neural Networks

Dazhuo Qiu, Mengying Wang, Arijit Khan, Yinghui Wu

Abstract: This paper introduces a new class of explanation structures, called robust counterfactual witnesses (RCWs), to provide robust, both counterfactual and factual explanations for graph neural networks. Given a graph neural network $\mathcal{M}$, a robust counterfactual witness refers to the fraction of a graph $G$ that are counterfactual and factual explanation of the results of $\mathcal{M}$ over $G$, but also remains so for any “disturbed” $G$ by flipping up to $k$ of its node pairs. We establish the hardness results, from tractable results to co-NP-hardness, for verifying and generating robust counterfactual witnesses. We study such structures for GNN-based node classification, and present efficient algorithms to verify and generate RCWs. We also provide a parallel algorithm to verify and generate RCWs for large graphs with scalability guarantees. We experimentally verify our explanation generation process for benchmark datasets, and showcase their applications.