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

• Main Authors: Haleem, Hammad, Wang, Yong, Puri, Abishek, Wadhwa, Sahil, Qu, Huamin
• Format: Journal Article
• Language: English
• Published: 14-11-2018
• Subjects: Computer Science - Computer Vision and Pattern Recognition; Computer Science - Graphics; Computer Science - Human-Computer Interaction
• DOI: 10.48550/arxiv.1808.00703

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.