Permutation Equivariant Generative Adversarial Networks for Graphs
- 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.
| Author: | Yoann Boget, Magda Gregorová, Alexandros Kalousis |
|---|---|
| DOI: | https://doi.org/10.48550/arXiv.2112.03621 |
| Parent Title (English): | Neural Compression Workshop (CoRR) |
| Document Type: | Conference Proceeding |
| Language: | English |
| Year of publication: | 2021 |
| Release Date: | 2024/01/22 |
| Volume: | abs/2112.03621 |
| Institutes and faculty: | Fakultäten / Fakultät Informatik und Wirtschaftsinformatik |
| Institute / Center for Artificial Intelligence (CAIRO) |
