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Permutation Equivariant Generative Adversarial Networks for Graphs

arXiv 2112.03621 ↗First posted 7 Dec 2021cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 20 of its 26 references that are in the map. First posted on arXiv 7 Dec 2021.

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References · 20

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8 more
  1. 2018Learning Deep Generative Models of Graphscited by 229
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