# Exploring the Limits of Complexity: A Survey of Empirical Studies on Graph Visualisation

arXiv: https://arxiv.org/abs/1809.00270 · HTML: https://mlsanity.com/paper/1809.00270/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2018.

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

- [Graph Neural Networks for Graph Drawing](https://mlsanity.com/paper/2109.10061/) (2021)

## Abstract (arXiv)

For decades, researchers in information visualisation and graph drawing have focused on developing techniques for the layout and display of very large and complex networks. Experiments involving human participants have also explored the readability of different styles of layout and representations for such networks. In both bodies of literature, networks are frequently referred to as being 'large' or 'complex', yet these terms are relative. From a human-centred, experiment point-of-view, what constitutes 'large' (for example) depends on several factors, such as data complexity, visual complexity, and the technology used. In this paper, we survey the literature on human-centred experiments to understand how, in practice, different features and characteristics of node-link diagrams affect visual complexity.