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Dettaglio pubblicazione

2022, The 25th International Conference on Artificial Intelligence and Statistics, Pages -

CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks (04b Atto di convegno in volume)

Lucic Ana, ter Hoeve Maartje, Tolomei Gabriele, de Rijke Maarten, Silvestri Fabrizio

Existing methods for interpreting predictions from Graph Neural Networks (GNNs) have primarily focused on generating subgraphs that are especially relevant for a particular prediction. However, such methods do not provide a clear opportunity for recourse: given a prediction, we want to understand how the prediction can be changed in order to achieve a more desirable outcome. In this work, we propose a method for generating counterfac- tual (CF) explanations for GNNs: the mini- mal 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 explana- tions 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 predic- tions, resulting in minimal CF explanations.
Gruppo di ricerca: Algorithms and Data Science, Gruppo di ricerca: Theory of Deep Learning
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