Semifactual Explanations for GNN-based Classification: Formal Foundations, Complexity and Computation

Abstract

Graph Neural Networks (GNNs) have become increasingly central to several analysis tasks in various domains, ranging, e.g., from social networks to molecular analysis. Understanding the decision-making process of GNNs is a critical challenge in machine learning, particularly when post-hoc explanations, attempting to answer why specific inputs are classified in a certain way by a given model, are required for specific input predictions. Existing post-hoc explainability methods for GNNs often rely on counterfactual reasoning, grounded in the `if this had not occurred' thinking, that typically identifies minimal subgraphs perturbations that would change a prediction. Notably, less attention has been posed on the equally important semifactual reasoning, grounded in the `even if this has not occurred' thinking, that results in identifying maximal subgraphs perturbations that would not change a prediction. In this paper, we introduce a novel technique for post-hoc explainability queries in GNNs by focusing on the semifactual reasoning. We first thoroughly investigate the computational complexity of several reasoning and optimization problems related to the computation of semifactuals, and then propose a novel learning architecture for addressing their computation. Finally, we experimentally evaluate the proposed approach in a variety of settings, showing its effectiveness in generating high-quality semifactual explanations.