One important benefit of the GAN-based framework is that it removes the complication of specifying a node ordering in the loss computation. As long as the discriminator model is permutation invariant, the GAN approach does not require any node ordering to be specified.

However, GAN-based approaches to graph generation have so far received less attention and success than their variational counterparts. This is likely due to the difficulties involved in the minimax optimization that GAN-based approaches require, and investigating the limits of GAN-based graph generation is currently an open problem.