# Can GAN Learn Topological Features of a Graph?

arXiv: https://arxiv.org/abs/1707.06197 · HTML: https://mlsanity.com/paper/1707.06197/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2017.

## Built on by (most cited first, 1 of 1)

- [SteinGen: Generating Fidelitous and Diverse Graph Samples](https://mlsanity.com/paper/2403.18578/) (2024)

## Abstract (arXiv)

This paper is first-line research expanding GANs into graph topology analysis. By leveraging the hierarchical connectivity structure of a graph, we have demonstrated that generative adversarial networks (GANs) can successfully capture topological features of any arbitrary graph, and rank edge sets by different stages according to their contribution to topology reconstruction. Moreover, in addition to acting as an indicator of graph reconstruction, we find that these stages can also preserve important topological features in a graph.