Graph neural networks (GNNs) have various practical applications, such as drug discovery, recommendation engines, and chip design. However, GNNs lack transparency as they cannot provide understandable explanations for their predictions. To address this issue, counterfactual reasoning is used. The main goal is to make minimal changes to the input graph of a GNN in order to alter its prediction. While several algorithms have been proposed for counterfactual explanations of GNNs, most of them have two main drawbacks. Firstly, they only consider edge deletions as perturbations. Secondly, the counterfactual explanation models are transductive, meaning they do not generalize to unseen data. In this study, we introduce an inductive algorithm called INDUCE, which overcomes these limitations. By conducting extensive experiments on several datasets, we demonstrate that incorporating edge additions leads to better counterfactual results compared to the existing methods. Moreover, the inductive modeling approach allows INDUCE to directly predict counterfactual perturbations without requiring instance-specific training. This results in significant computational speed improvements compared to baseline methods and enables scalable counterfactual analysis for GNNs.

翻译：图神经网络（GNN）在药物发现、推荐引擎和芯片设计等实际应用中具有广泛用途。然而，GNN因无法对其预测结果提供可解释的说明而缺乏透明度。为解决这一问题，研究者采用了反事实推理方法，其核心目标是通过对GNN输入图进行最小化修改来改变其预测结果。尽管已有多种针对GNN反事实解释的算法被提出，但大多数存在两个主要缺陷：其一，它们仅将边删除视为扰动形式；其二，反事实解释模型具有直推式特性，无法泛化至未见数据。本研究提出一种名为INDUCE的归纳式算法，有效克服了上述局限性。通过在多个数据集上进行广泛实验，我们证明相较于现有方法，引入边添加操作能获得更优的反事实结果。此外，归纳式建模方法使INDUCE能够直接预测反事实扰动，无需实例特定训练，与基线方法相比实现了显著的运算速度提升，并支持GNN的可扩展反事实分析。