Evaluating the readability of force directed graph layouts: A deep learning approach
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 the...
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sg-smu-ink.sis_research-63792020-12-02T04:44:39Z Evaluating the readability of force directed graph layouts: A deep learning approach HALEEM, Hammad WANG, Yong PURI, Abishek WADHWA, Sahil QU, Huamin 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 by showing usage scenarios and visualizing convolutional layers. This work is a first step towards using deep learning based methods to quantitatively evaluate images from the visualization field. 2019-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/5375 info:doi/10.1109/MCG.2018.2881501 https://ink.library.smu.edu.sg/context/sis_research/article/6379/viewcontent/evaluating__PV.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Graphics and Human Computer Interfaces Software Engineering |
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Graphics and Human Computer Interfaces Software Engineering HALEEM, Hammad WANG, Yong PURI, Abishek WADHWA, Sahil QU, Huamin Evaluating the readability of force directed graph layouts: A deep learning approach |
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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 by showing usage scenarios and visualizing convolutional layers. This work is a first step towards using deep learning based methods to quantitatively evaluate images from the visualization field. |
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text |
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HALEEM, Hammad WANG, Yong PURI, Abishek WADHWA, Sahil QU, Huamin |
author_facet |
HALEEM, Hammad WANG, Yong PURI, Abishek WADHWA, Sahil QU, Huamin |
author_sort |
HALEEM, Hammad |
title |
Evaluating the readability of force directed graph layouts: A deep learning approach |
title_short |
Evaluating the readability of force directed graph layouts: A deep learning approach |
title_full |
Evaluating the readability of force directed graph layouts: A deep learning approach |
title_fullStr |
Evaluating the readability of force directed graph layouts: A deep learning approach |
title_full_unstemmed |
Evaluating the readability of force directed graph layouts: A deep learning approach |
title_sort |
evaluating the readability of force directed graph layouts: a deep learning approach |
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Institutional Knowledge at Singapore Management University |
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2019 |
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https://ink.library.smu.edu.sg/sis_research/5375 https://ink.library.smu.edu.sg/context/sis_research/article/6379/viewcontent/evaluating__PV.pdf |
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