Counterfactual explainability (CE) has been widely explored in various domains ranging from medical image diagnosis to self-driving cars. Graph CE (GCE), on the other hand, and especially, generative-based GCE has yet to be explored. Here, we adapt CounteRGAN, an image-based generative approach, to consider graph adjacency matrices as special black-and-white images and sample valid counterfactuals directly from the learnt latent space probabilistic distribution.