# Toward automatic comparison of visualization techniques: Application to graph visualization

arXiv: https://arxiv.org/abs/1910.09477 · HTML: https://mlsanity.com/paper/1910.09477/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2019.

## Built on by (most cited first, 8 of 8)

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- [Generalization of CNNs on Relational Reasoning with Bar Charts](https://mlsanity.com/paper/2503.00086/) (2025)
- [TopER: Topological Embeddings in Graph Representation Learning](https://mlsanity.com/paper/2410.01778/) (2024)
- [Impacts of the Numbers of Colors and Shapes on Outlier Detection: from Automated to User Evaluation](https://mlsanity.com/paper/2103.06084/) (2021)
- [Can an NN model plainly learn planar layouts?](https://mlsanity.com/paper/2209.01075/) (2022)

## Abstract (arXiv)

Many end-user evaluations of data visualization techniques have been run during the last decades. Their results are cornerstones to build efficient visualization systems. However, designing such an evaluation is always complex and time-consuming and may end in a lack of statistical evidence and reproducibility. We believe that modern and efficient computer vision techniques, such as deep convolutional neural networks (CNNs), may help visualization researchers to build and/or adjust their evaluation hypothesis. The basis of our idea is to train machine learning models on several visualization techniques to solve a specific task. Our assumption is that it is possible to compare the efficiency of visualization techniques based on the performance of their corresponding model. As current machine learning models are not able to strictly reflect human capabilities, including their imperfections, such results should be interpreted with caution. However, we think that using machine learning-based pre-evaluation, as a pre-process of standard user evaluations, should help researchers to perform a more exhaustive study of their design space. Thus, it should improve their final user evaluation by providing it better test cases. In this paper, we present the results of two experiments we have conducted to assess how correlated the performance of users and computer vision techniques can be. That study compares two mainstream graph visualization techniques: node-link (NL) and adjacency-matrix (MD) diagrams. Using two well-known deep convolutional neural networks, we partially reproduced user evaluations from Ghoniem textitet al. and from Okoe textitet al.. These experiments showed that some user evaluation results can be reproduced automatically.