Visualizing big graphs with labels through edge bundling

Wu Quan Wang; Zhuang Cai; Kang Zhang

doi:10.1109/ICBDA.2016.7509837

Visualizing big graphs with labels through edge bundling

Wu Quan Wang, Zhuang Cai, Kang Zhang

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

1 Citation (Scopus)

Abstract

Graphs depicted as node-link diagrams are widely used in visualizing relational data sets. Visual clutter becomes problematic with big node-link diagrams. Researchers have recently proposed various approaches to bundling edges to reduce visual clutter. None of the edge bundling approaches, however, addresses the issue of labeling in the visualized graph. This paper presents an approach to bundling edges of big graphs, possibly over geographical maps with location labels such as cities and states. Our approach routes edges via flexible control points to leave space for labels to be clearly visible. We demonstrate the effectiveness of our approach on two commonly used datasets.

Original language	English
Title of host publication	Proceedings of 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781467395908
DOIs	https://doi.org/10.1109/ICBDA.2016.7509837
Publication status	Published - 12 Jul 2016
Externally published	Yes
Event	2016 IEEE International Conference on Big Data Analysis, ICBDA 2016 - Hangzhou, China Duration: 12 Mar 2016 → 14 Mar 2016

Publication series

Name	Proceedings of 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016

Conference

Conference	2016 IEEE International Conference on Big Data Analysis, ICBDA 2016
Country/Territory	China
City	Hangzhou
Period	12/03/16 → 14/03/16

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

big graph
clutter reduction
edge bundling
information visualisation
labeled map

Access to Document

10.1109/ICBDA.2016.7509837

Cite this

Wang, W. Q., Cai, Z., & Zhang, K. (2016). Visualizing big graphs with labels through edge bundling. In Proceedings of 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016 Article 7509837 (Proceedings of 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICBDA.2016.7509837

@inproceedings{32c44c5b5d344b55b48e4341ffb8c09d,

title = "Visualizing big graphs with labels through edge bundling",

abstract = "Graphs depicted as node-link diagrams are widely used in visualizing relational data sets. Visual clutter becomes problematic with big node-link diagrams. Researchers have recently proposed various approaches to bundling edges to reduce visual clutter. None of the edge bundling approaches, however, addresses the issue of labeling in the visualized graph. This paper presents an approach to bundling edges of big graphs, possibly over geographical maps with location labels such as cities and states. Our approach routes edges via flexible control points to leave space for labels to be clearly visible. We demonstrate the effectiveness of our approach on two commonly used datasets.",

keywords = "big graph, clutter reduction, edge bundling, information visualisation, labeled map",

author = "Wang, \{Wu Quan\} and Zhuang Cai and Kang Zhang",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016 ; Conference date: 12-03-2016 Through 14-03-2016",

year = "2016",

month = jul,

day = "12",

doi = "10.1109/ICBDA.2016.7509837",

language = "English",

series = "Proceedings of 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "Proceedings of 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016",

}

Wang, WQ, Cai, Z & Zhang, K 2016, Visualizing big graphs with labels through edge bundling. in Proceedings of 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016., 7509837, Proceedings of 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016, Institute of Electrical and Electronics Engineers Inc., 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016, Hangzhou, China, 12/03/16. https://doi.org/10.1109/ICBDA.2016.7509837

Visualizing big graphs with labels through edge bundling. / Wang, Wu Quan; Cai, Zhuang; Zhang, Kang.
Proceedings of 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016. Institute of Electrical and Electronics Engineers Inc., 2016. 7509837 (Proceedings of 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016).

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

TY - GEN

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AU - Wang, Wu Quan

AU - Cai, Zhuang

AU - Zhang, Kang

PY - 2016/7/12

Y1 - 2016/7/12

N2 - Graphs depicted as node-link diagrams are widely used in visualizing relational data sets. Visual clutter becomes problematic with big node-link diagrams. Researchers have recently proposed various approaches to bundling edges to reduce visual clutter. None of the edge bundling approaches, however, addresses the issue of labeling in the visualized graph. This paper presents an approach to bundling edges of big graphs, possibly over geographical maps with location labels such as cities and states. Our approach routes edges via flexible control points to leave space for labels to be clearly visible. We demonstrate the effectiveness of our approach on two commonly used datasets.

AB - Graphs depicted as node-link diagrams are widely used in visualizing relational data sets. Visual clutter becomes problematic with big node-link diagrams. Researchers have recently proposed various approaches to bundling edges to reduce visual clutter. None of the edge bundling approaches, however, addresses the issue of labeling in the visualized graph. This paper presents an approach to bundling edges of big graphs, possibly over geographical maps with location labels such as cities and states. Our approach routes edges via flexible control points to leave space for labels to be clearly visible. We demonstrate the effectiveness of our approach on two commonly used datasets.

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KW - clutter reduction

KW - edge bundling

KW - information visualisation

KW - labeled map

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DO - 10.1109/ICBDA.2016.7509837

M3 - Conference Paper published in a book

AN - SCOPUS:84981298131

T3 - Proceedings of 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016

BT - Proceedings of 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2016 IEEE International Conference on Big Data Analysis, ICBDA 2016

Y2 - 12 March 2016 through 14 March 2016

ER -

Visualizing big graphs with labels through edge bundling

Abstract

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Bibliographical note

Keywords

Access to Document

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