2025-01-12-hansen25a.md

title	openreview	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Graph Counterfactual Explainable AI via Latent Space Traversal	Pyqnc9eWhB	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. \textit{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.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	hansen25a	0	Graph Counterfactual Explainable {AI} via Latent Space Traversal	75	84	75-84	75	false	Hansen, Andreas Abildtrup and Pegios, Paraskevas and Calissano, Anna and Feragen, Aasa	given family Andreas Abildtrup Hansen given family Paraskevas Pegios given family Anna Calissano given family Aasa Feragen	2025-01-12		Proceedings of the 6th Northern Lights Deep Learning Conference (NLDL)	265	inproceedings	date-parts 2025 1 12	https://raw.githubusercontent.com/mlresearch/v265/main/assets/hansen25a/hansen25a.pdf