Drawing large weighted graphs using clustered force-directed algorithm

Jie Hua; Mao Lin Huang; Quang Vinh Nguyen

doi:10.1109/IV.2014.24

Drawing large weighted graphs using clustered force-directed algorithm

Jie Hua, Mao Lin Huang, Quang Vinh Nguyen

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

5 Citations (Scopus)

Abstract

![CDATA[Clustered graph drawing is widely considered as a good method to overcome the scalability problem when visualizing large (or huge) graphs. Force-directed algorithm is a popular approach for laying graphs yet small to medium size datasets due to its slow convergence time. This paper proposes a new method which combines clustering and a force-directed algorithm, to reduce the computational complexity and time. It works by dividing a Long Convergence: LC into two Short Convergences: SC1, SC2, where SC1+SC2 < LC. We also apply our work on weighted graphs. Our experiments show that the new method improves the aesthetics in graph visualization by providing clearer views for connectivity and edge weights.]]

Original language	English
Title of host publication	Proceedings of the 2014 18th International Conference on Information Visualisation (IV 2014), 16-18 July 2014, Paris, France
Publisher	IEEE
Pages	13-17
Number of pages	5
ISBN (Print)	9781479941032
DOIs	https://doi.org/10.1109/IV.2014.24
Publication status	Published - 2014
Event	International Conference on Information Visualisation - Duration: 16 Jul 2014 → …

Conference

Conference	International Conference on Information Visualisation
Period	16/07/14 → …

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10.1109/IV.2014.24

Cite this

@inproceedings{196076e646264ee8a5428af04acbcceb,

title = "Drawing large weighted graphs using clustered force-directed algorithm",

abstract = "![CDATA[Clustered graph drawing is widely considered as a good method to overcome the scalability problem when visualizing large (or huge) graphs. Force-directed algorithm is a popular approach for laying graphs yet small to medium size datasets due to its slow convergence time. This paper proposes a new method which combines clustering and a force-directed algorithm, to reduce the computational complexity and time. It works by dividing a Long Convergence: LC into two Short Convergences: SC1, SC2, where SC1+SC2 < LC. We also apply our work on weighted graphs. Our experiments show that the new method improves the aesthetics in graph visualization by providing clearer views for connectivity and edge weights.]]",

author = "Jie Hua and Huang, {Mao Lin} and Nguyen, {Quang Vinh}",

year = "2014",

doi = "10.1109/IV.2014.24",

language = "English",

isbn = "9781479941032",

pages = "13--17",

booktitle = "Proceedings of the 2014 18th International Conference on Information Visualisation (IV 2014), 16-18 July 2014, Paris, France",

publisher = "IEEE",

note = "International Conference on Information Visualisation ; Conference date: 16-07-2014",

}

TY - GEN

T1 - Drawing large weighted graphs using clustered force-directed algorithm

AU - Hua, Jie

AU - Huang, Mao Lin

AU - Nguyen, Quang Vinh

PY - 2014

Y1 - 2014

N2 - ![CDATA[Clustered graph drawing is widely considered as a good method to overcome the scalability problem when visualizing large (or huge) graphs. Force-directed algorithm is a popular approach for laying graphs yet small to medium size datasets due to its slow convergence time. This paper proposes a new method which combines clustering and a force-directed algorithm, to reduce the computational complexity and time. It works by dividing a Long Convergence: LC into two Short Convergences: SC1, SC2, where SC1+SC2 < LC. We also apply our work on weighted graphs. Our experiments show that the new method improves the aesthetics in graph visualization by providing clearer views for connectivity and edge weights.]]

AB - ![CDATA[Clustered graph drawing is widely considered as a good method to overcome the scalability problem when visualizing large (or huge) graphs. Force-directed algorithm is a popular approach for laying graphs yet small to medium size datasets due to its slow convergence time. This paper proposes a new method which combines clustering and a force-directed algorithm, to reduce the computational complexity and time. It works by dividing a Long Convergence: LC into two Short Convergences: SC1, SC2, where SC1+SC2 < LC. We also apply our work on weighted graphs. Our experiments show that the new method improves the aesthetics in graph visualization by providing clearer views for connectivity and edge weights.]]

UR - http://handle.uws.edu.au:8081/1959.7/554215

UR - http://www.graphicslink.co.uk/IV2014/

U2 - 10.1109/IV.2014.24

DO - 10.1109/IV.2014.24

M3 - Conference Paper

SN - 9781479941032

SP - 13

EP - 17

BT - Proceedings of the 2014 18th International Conference on Information Visualisation (IV 2014), 16-18 July 2014, Paris, France

PB - IEEE

T2 - International Conference on Information Visualisation

Y2 - 16 July 2014

ER -

Drawing large weighted graphs using clustered force-directed algorithm

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