  Counterfactual Explanation (CE) techniques have garnered attention as a means
to provide insights to the users engaging with AI systems. While extensively
researched in domains such as medical imaging and autonomous vehicles, Graph
Counterfactual Explanation (GCE) methods have been comparatively
under-explored. GCEs generate a new graph similar to the original one, with a
different outcome grounded on the underlying predictive model. Among these GCE
techniques, those rooted in generative mechanisms have received relatively
limited investigation despite demonstrating impressive accomplishments in other
domains, such as artistic styles and natural language modelling. The preference
for generative explainers stems from their capacity to generate counterfactual
instances during inference, leveraging autonomously acquired perturbations of
the input graph. Motivated by the rationales above, our study introduces
RSGG-CE, a novel Robust Stochastic Graph Generator for Counterfactual
Explanations able to produce counterfactual examples from the learned latent
space considering a partially ordered generation sequence. Furthermore, we
undertake quantitative and qualitative analyses to compare RSGG-CE's
performance against SoA generative explainers, highlighting its increased
ability to engendering plausible counterfactual candidates.
