Given the increasing promise of graph neural networks (GNNs) in real-world applications, several methods have been developed for explaining their predictions. Existing methods for interpreting predictions from GNNs have primarily focused on generating subgraphs that are especially relevant for a particular prediction. However, such methods are not counterfactual (CF) in nature: given a prediction, we want to understand how the prediction can be changed in order to achieve an alternative outcome. In this work, we propose a method for generating CF explanations for GNNs: the minimal perturbation to the input (graph) data such that the prediction changes. Using only edge deletions, we find that our method, CF-GNNExplainer, can generate CF explanations for the majority of instances across three widely used datasets for GNN explanations, while removing less than 3 edges on average, with at least 94\% accuracy. This indicates that CF-GNNExplainer primarily removes edges that are crucial for the original predictions, resulting in minimal CF explanations.
@article{arxiv.2102.03322,
title = {CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks},
author = {Ana Lucic and Maartje ter Hoeve and Gabriele Tolomei and Maarten de Rijke and Fabrizio Silvestri},
journal= {arXiv preprint arXiv:2102.03322},
year = {2022}
}