# Can an NN model plainly learn planar layouts?

arXiv: https://arxiv.org/abs/2209.01075 · HTML: https://mlsanity.com/paper/2209.01075/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 2 Sep 2022.

## Builds on (most cited first, 6 of 6)

- [A Deep Generative Model for Graph Layout](https://mlsanity.com/paper/1904.12225/) (2019)
- [DeepDrawing: A Deep Learning Approach to Graph Drawing](https://mlsanity.com/paper/1907.11040/) (2019)
- [Evaluating the Readability of Force Directed Graph Layouts: A Deep Learning Approach](https://mlsanity.com/paper/1808.00703/) (2018)
- [Toward automatic comparison of visualization techniques: Application to graph visualization](https://mlsanity.com/paper/1910.09477/) (2019)
- [Deep Neural Network for DrawiNg Networks, (DNN)^2](https://mlsanity.com/paper/2108.03632/) (2021)
- [Aesthetic Discrimination of Graph Layouts](https://mlsanity.com/paper/1809.01017/) (2018)

## Abstract (arXiv)

Planar graph drawings tend to be aesthetically pleasing. In this poster we explore a Neural Network's capability of learning various planar graph classes. Additionally, we also investigate the effectiveness of the model in generalizing beyond planarity. We find that the model can outperform conventional techniques for certain graph classes. The model, however, appears to be more susceptible to randomness in the data, and seems to be less robust than expected.