Graph Counterfactual Explainable AI via Latent Space Traversal

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Abstract

Explaining the predictions of a deep neural network is a nontrivial task, yet high-quality explanations for predictions are often a prerequisite for practitioners to trust these models. Counterfactual explanations aim to explain predictions by finding the “nearest” in-distribution alternative input whose prediction changes in a pre-specified way. However, it remains an open question how to define this nearest alternative input, whose solution depends on both the domain (e.g. images, graphs, tabular data, etc.) and the specific application considered. For graphs, this problem is complicated i) by their discrete nature, as opposed to the continuous nature of state-of-the-art graph classifiers; and ii) by the node permutation group acting on the graphs. We propose a method to generate counterfactual explanations for any differentiable black-box graph classifier, utilizing a case-specific permutation equivariant graph variational autoencoder. We generate counterfactual explanations in a continuous fashion by traversing the latent space of the autoencoder across the classification boundary of the classifier, allowing for seamless integration of discrete graph structure and continuous graph attributes. We empirically validate the approach on three graph datasets, showing that our model is consistently high-performing and more robust than the baselines.
Original languageEnglish
Title of host publicationProceedings of the 6th Northern Lights Deep Learning Conference (NLDL)
Volume265
PublisherProceedings of Machine Learning Research
Publication date2025
Pages75-84
Publication statusPublished - 2025
Event6th Northern Lights Deep Learning Conference 2025 - Tromsø, Norway
Duration: 7 Jan 20259 Jan 2025

Conference

Conference6th Northern Lights Deep Learning Conference 2025
Country/TerritoryNorway
CityTromsø
Period07/01/202509/01/2025
SeriesProceedings of Machine Learning Research
ISSN2640-3498

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