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

TL;DR

This paper introduces a deep learning approach using CNNs to evaluate graph layout readability directly from images, overcoming limitations of traditional metrics that require node and edge coordinates.

Contribution

It presents a novel CNN-based method trained on a diverse graph image dataset to assess readability metrics without needing coordinate data.

Findings

The CNN approach accurately predicts readability metrics.

It outperforms traditional computational methods in efficiency.

The method enables quantitative evaluation from graph images alone.

Abstract

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.