Permutation Equivariant Generative Adversarial Networks for Graphs
This work addresses graph generation for machine learning applications, but it is incremental as the model is still under development and focuses on a known bottleneck.
The paper tackles the problem of graph generation by addressing the ordering issue in graph representations, proposing a 3-stage GAN model called 3G-GAN that uses equivariant functions to ensure invariance, with preliminary experiments showing encouraging results.
One of the most discussed issues in graph generative modeling is the ordering of the representation. One solution consists of using equivariant generative functions, which ensure the ordering invariance. After having discussed some properties of such functions, we propose 3G-GAN, a 3-stages model relying on GANs and equivariant functions. The model is still under development. However, we present some encouraging exploratory experiments and discuss the issues still to be addressed.