  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.
