# Aesthetic Discrimination of Graph Layouts

arXiv: https://arxiv.org/abs/1809.01017 · HTML: https://mlsanity.com/paper/1809.01017/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2018.

## Built on by (most cited first, 5 of 5)

- [DeepGD: A Deep Learning Framework for Graph Drawing Using GNN](https://mlsanity.com/paper/2106.15347/) (2021)
- [Ask and You Shall Receive (a Graph Drawing): Testing ChatGPT's Potential to Apply Graph Layout Algorithms](https://mlsanity.com/paper/2303.08819/) (2023)
- [A Machine Learning Approach for Predicting Human Preference for Graph Layouts](https://mlsanity.com/paper/2103.03665/) (2021)
- [Can an NN model plainly learn planar layouts?](https://mlsanity.com/paper/2209.01075/) (2022)
- [Beauty in the Eye of AI: Aligning LLMs and Vision Models with Human Aesthetics in Network Visualization](https://mlsanity.com/paper/2604.03417/) (2026)

## Abstract (arXiv)

This paper addresses the following basic question: given two layouts of the same graph, which one is more aesthetically pleasing? We propose a neural network-based discriminator model trained on a labeled dataset that decides which of two layouts has a higher aesthetic quality. The feature vectors used as inputs to the model are based on known graph drawing quality metrics, classical statistics, information-theoretical quantities, and two-point statistics inspired by methods of condensed matter physics. The large corpus of layout pairs used for training and testing is constructed using force-directed drawing algorithms and the layouts that naturally stem from the process of graph generation. It is further extended using data augmentation techniques. The mean prediction accuracy of our model is 95.70%, outperforming discriminators based on stress and on the linear combination of popular quality metrics by a statistically significant margin.