Drawing large weighted graphs using clustered force-directed algorithm

Hua, J; Huang, ML; Nguyen, QV

Drawing large weighted graphs using clustered force-directed algorithm

Hua, J

Huang, ML

Nguyen, QV

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings of the International Conference on Information Visualisation, 2014, pp. 13 - 17
Issue Date:: 2014-01-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Published versionAdobe PDF (224.05 kB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Hua, J https://orcid.org/0000-0002-3409-2076	en_US
dc.contributor.author	Huang, ML https://orcid.org/0000-0002-6896-6480	en_US
dc.contributor.author	Nguyen, QV	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Proceedings of the International Conference on Information Visualisation, 2014, pp. 13 - 17	en_US
dc.identifier.isbn	9781479941032	en_US
dc.identifier.issn	1093-9547	en_US
dc.identifier.uri	http://hdl.handle.net/10453/38164
dc.description.abstract	© 2014 IEEE. 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.	en_US
dc.relation.ispartof	Proceedings of the International Conference on Information Visualisation	en_US
dc.relation.isbasedon	10.1109/IV.2014.24	en_US
dc.title	Drawing large weighted graphs using clustered force-directed algorithm	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Paris
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

© 2014 IEEE. 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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/38164