Article

Node Overlap Removal Algorithms: A Comparative Study

Authors:

Laurent Piccinini,

Pascal Poncelet,

Arnaud SallaberryAuthors Info & Claims

Graph Drawing and Network Visualization: 27th International Symposium, GD 2019, Prague, Czech Republic, September 17–20, 2019, Proceedings

Pages 179 - 192

https://doi.org/10.1007/978-3-030-35802-0_14

Published: 17 September 2019 Publication History

Abstract

Many algorithms have been designed to remove node overlapping, and many quality criteria and associated metrics have been proposed to evaluate those algorithms. Unfortunately, a complete comparison of the algorithms based on some metrics that evaluate the quality has never been provided and it is thus difficult for a visualization designer to select the algorithm that best suits his needs. In this paper, we review 21 metrics available in the literature, classify them according to the quality criteria they try to capture, and select a representative one for each class. Based on the selected metrics, we compare 8 node overlap removal algorithms. Our experiment involves 854 synthetic and real-world graphs.

References

[1]

Barabaśi AL and Albert R Emergence of scaling in random networks Science 1999 286 5439 509-512

[2]

Chimani M, Gutwenger C, Jünger M, Klau GW, Klein K, and Mutzel P Tamassia R The open graph drawing framework (OGDF) Handbook on Graph Drawing and Visualization 2013 London Chapman and Hall/CRC 543-569

[3]

Dwyer T, Marriott K, and Stuckey PJ Healy P and Nikolov NS Fast node overlap removal Graph Drawing 2006 Heidelberg Springer 153-164

[4]

Erdös P and Rényi A On random graphs. Publicationes Mathematicae Debrecen 1959 6 290-291

[5]

Gansner E and Hu Y Efficient, proximity-preserving node overlap removal J. Graph Algorithms Appl. 2010 14 1 53-74

[6]

Gansner ER and North SC An open graph visualization system and its applications to software engineering Softw. Pract. Exp. 2000 30 11 1203-1233

[7]

Hachul S and Jünger M Pach J Drawing large graphs with a potential-field-based multilevel algorithm Graph Drawing 2005 Heidelberg Springer 285-295

[8]

Hayashi K, Inoue M, Masuzawa T, and Fujiwara H Whitesides SH A layout adjustment problem for disjoint rectangles preserving orthogonal order Graph Drawing 1998 Heidelberg Springer 183-197

[9]

Hu Y Efficient, high-quality force-directed graph drawing Math. J. 2005 10 1 37-71

[10]

Huang, X., Lai, W.: Force-transfer: a new approach to removing overlapping nodes in graph layout. In: Proceedings of the 26th Australasian computer science conference, vol. 16, pp. 349–358. Australian Computer Society, Inc. (2003)

[11]

Huang X, Lai W, Sajeev A, and Gao J A new algorithm for removing node overlapping in graph visualization Inf. Sci. 2007 177 14 2821-2844

[12]

Li, W., Eades, P., Nikolov, N.: Using spring algorithms to remove node overlapping. In: Proceedings of the 2005 Asia-Pacific Symposium on Information Visualisation, vol. 45, pp. 131–140. APVis 2005. Australian Computer Society Inc, Darlinghurst (2005)

[13]

Lyons KA, Meijer H, and Rappaport D Algorithms for cluster busting in anchored graph drawing J. Graph Algorithms Appl. 1998 2 1 1-24

[14]

Marriott K, Stuckey P, Tam V, and He W Removing node overlapping in graph layout using constrained optimization Constraints 2003 8 2 143-171

[15]

Misue K, Eades P, Lai W, and Sugiyama K Layout adjustment and the mental map J. Vis. Lang. Comput. 1995 6 2 183-210

[16]

Nachmanson L, Nocaj A, Bereg S, Zhang L, and Holroyd A Hu Y and Nöllenburg M Node overlap removal by growing a tree Graph Drawing and Network Visualization 2016 Cham Springer 33-43

[17]

Strobelt H, Spicker M, Stoffel A, Keim D, and Deussen O Rolled-out wordles: a heuristic method for overlap removal of 2D data representatives Comput. Graph. Forum 2012 31 3 1135-1144

[18]

Watts DJ and Strogatz SH Collective dynamics of ‘small-world’ networks Nature 1998 393 440-442

Cited By

Giovannangeli LLalanne FGiot RBourqui R(2024)Guaranteed Visibility in Scatterplots with ToleranceIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332659630:1(792-802)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TVCG.2023.3326596
Giovannangeli LLalanne FGiot RBourqui R(2022)FORBID: Fast Overlap Removal by Stochastic GradIent Descent for Graph DrawingGraph Drawing and Network Visualization10.1007/978-3-031-22203-0_6(61-76)Online publication date: 13-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-22203-0_6

Index Terms

Node Overlap Removal Algorithms: A Comparative Study

Index terms have been assigned to the content through auto-classification.

Recommendations

FORBID: Fast Overlap Removal by Stochastic GradIent Descent for Graph Drawing
Graph Drawing and Network Visualization
Abstract
While many graph drawing algorithms consider nodes as points, graph visualization tools often represent them as shapes. These shapes support the display of information such as labels or encode various data with size or color. However, they can ...
A Comparative Study of Staff Removal Algorithms

This paper presents a quantitative comparison of different algorithms for the removal of stafflines from music images. It contains a survey of previously proposed algorithms and suggests a new skeletonization based approach. We define three different ...
Efficient Node Overlap Removal Using a Proximity Stress Model
Graph Drawing

When drawing graphs whose nodes contain text or graphics, the nontrivial node sizes must be taken into account, either as part of the initial layout or as a post-processing step. The core problem is to avoid overlaps while retaining the structural ...

Comments

Information & Contributors

Information

Published In

cover image Guide Proceedings

Graph Drawing and Network Visualization: 27th International Symposium, GD 2019, Prague, Czech Republic, September 17–20, 2019, Proceedings

Sep 2019

583 pages

ISBN:978-3-030-35801-3

DOI:10.1007/978-3-030-35802-0

Editors:
Daniel Archambault
Swansea University, Swansea, UK
,
Csaba D. Tóth
California State University, Northridge, Los Angeles, CA, USA

© Springer Nature Switzerland AG 2019.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 17 September 2019

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 22 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Giovannangeli LLalanne FGiot RBourqui R(2024)Guaranteed Visibility in Scatterplots with ToleranceIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332659630:1(792-802)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TVCG.2023.3326596
Giovannangeli LLalanne FGiot RBourqui R(2022)FORBID: Fast Overlap Removal by Stochastic GradIent Descent for Graph DrawingGraph Drawing and Network Visualization10.1007/978-3-031-22203-0_6(61-76)Online publication date: 13-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-22203-0_6

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents