# Evaluating the Readability of Force Directed Graph Layouts: A Deep Learning Approach

arXiv: https://arxiv.org/abs/1808.00703 · HTML: https://mlsanity.com/paper/1808.00703/

## Key facts

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

## Built on by (most cited first, 14 of 14)

- [DeepGD: A Deep Learning Framework for Graph Drawing Using GNN](https://mlsanity.com/paper/2106.15347/) (2021)
- [Deep Neural Network for DrawiNg Networks, (DNN)^2](https://mlsanity.com/paper/2108.03632/) (2021)
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- [Physics-Informed Graph Learning](https://mlsanity.com/paper/2202.10679/) (2022)
- [Reinforced Labels: Multi-Agent Deep Reinforcement Learning for Point-Feature Label Placement](https://mlsanity.com/paper/2303.01388/) (2023)
- [A Machine Learning Approach for Predicting Human Preference for Graph Layouts](https://mlsanity.com/paper/2103.03665/) (2021)
- [Interactive Visual Pattern Search on Graph Data via Graph Representation Learning](https://mlsanity.com/paper/2202.09459/) (2022)
- [Multi-dimensional Data Analysis and Applications Basing on LLM Agents and Knowledge Graph Interactions](https://mlsanity.com/paper/2510.15258/) (2025)
- [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)
- [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)

Existing graph layout algorithms are usually not able to optimize all the aesthetic properties desired in a graph layout. To evaluate how well the desired visual features are reflected in a graph layout, many readability metrics have been proposed in the past decades. However, the calculation of these readability metrics often requires access to the node and edge coordinates and is usually computationally inefficient, especially for dense graphs. Importantly, when the node and edge coordinates are not accessible, it becomes impossible to evaluate the graph layouts quantitatively. In this paper, we present a novel deep learning-based approach to evaluate the readability of graph layouts by directly using graph images. A convolutional neural network architecture is proposed and trained on a benchmark dataset of graph images, which is composed of synthetically-generated graphs and graphs created by sampling from real large networks. Multiple representative readability metrics (including edge crossing, node spread, and group overlap) are considered in the proposed approach. We quantitatively compare our approach to traditional methods and qualitatively evaluate our approach using a case study and visualizing convolutional layers. This work is a first step towards using deep learning based methods to evaluate images from the visualization field quantitatively.