Back to Search Start Over

NeatSankey: Sankey diagrams with improved readability based on node positioning and edge bundling.

Authors :
Xu, Zhe
Zhou, Buwei
Yang, Zhou
Yuan, Xiaohui
Zhang, Yankong
Lu, Qiang
Source :
Computers & Graphics. Jun2023, Vol. 113, p10-20. 11p.
Publication Year :
2023

Abstract

Sankey diagrams are widely used to visualize event sequence data. However, when the data volume is large, its readability is affected by dense edge crossings, excessive swing amplitude, and small crossover angles, while it is computationally intensive to obtain an optimal layout. In this paper, we propose NeatSankey, a balanced method that generates Sankey diagrams smoothly. It can be laid out quickly with good readability when Sankey diagrams are very complex. At the same time, to comprehensively evaluate the readability of Sankey diagrams, we use three evaluation metrics: crossing number, swing amplitude, and layout coverage. Firstly, we use a heuristic layout algorithm and a force-directed algorithm to adjust the node layout to minimize the edge crossings and swing amplitude with edge widths considered. Secondly, to better reduce the dense confusion caused by edge crossings, we introduce a edge bundling algorithm based on attribute similarity. We present three evaluations: a comprehensive comparison of our results with state-of-the-art techniques, user studies with thirty volunteers, and a case study of two datasets. Our evaluations demonstrate the effectiveness and practicability of the NeatSankey. [Display omitted] • We propose a novel two-step algorithm to enhance the readability of Sankey diagrams. • NeatSankey can generate Sankey diagrams smoothly within a limited time. • We can bundle edges with different widths and show the exact connections. • We design evaluations which can measure the readability of Sankey diagrams perfectly. • We conduct three studies to show the effectiveness and practicability of NeatSankey. [ABSTRACT FROM AUTHOR]

Subjects

Subjects :
*ALGORITHMS
*VOLUNTEERS

Details

Language :
English
ISSN :
00978493
Volume :
113
Database :
Academic Search Index
Journal :
Computers & Graphics
Publication Type :
Academic Journal
Accession number :
164382293
Full Text :
https://doi.org/10.1016/j.cag.2023.05.001