Eigenedge: A measure of edge centrality for big graph exploration

Huang, X; Huang, W

Eigenedge: A measure of edge centrality for big graph exploration

Huang, X Huang, W

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Publication Type:: Journal Article
Citation:: Journal of Computer Languages, 2019, 55
Issue Date:: 2019-12-01

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Field	Value	Language
dc.contributor.author	Huang, X	en_US
dc.contributor.author	Huang, W https://orcid.org/0000-0002-5190-7839	en_US
dc.date.issued	2019-12-01	en_US
dc.identifier.citation	Journal of Computer Languages, 2019, 55	en_US
dc.identifier.issn	2665-9182	en_US
dc.identifier.uri	http://hdl.handle.net/10453/137393
dc.description.abstract	© 2019 Elsevier Ltd As we are in the age of big data, graph data become bigger. A big graph normally has an overwhelming number of edges. Existing metrics of edge centrality are not quite suitable for dealing with such a large graph. A novel metric for measuring the importance of edges in a graph is introduced in this paper. Compared with the other six popular matrices with respect to a number of real-world graphs, the proposed metric is capable of capturing the structural feature of a graph in a scalable way. The comprehensive experiments have demonstrated the performance of the proposed metric. According to this metric, a filtering approach is presented to reduce the visual clutter of a layout in a way that the hidden patterns can be revealed gradually. As evidenced by real-world examples, our approach allows users to explore graphs in real-time with a high level of details in an interactive way in order to gain insight into graph data.	en_US
dc.relation.ispartof	Journal of Computer Languages	en_US
dc.relation.isbasedon	10.1016/j.cola.2019.100925	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Eigenedge: A measure of edge centrality for big graph exploration	en_US
dc.type	Journal Article
utslib.citation.volume	55	en_US
utslib.for	0102 Applied Mathematics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Transdisciplinary Innovation
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US
pubs.volume	55	en_US

Abstract:

© 2019 Elsevier Ltd As we are in the age of big data, graph data become bigger. A big graph normally has an overwhelming number of edges. Existing metrics of edge centrality are not quite suitable for dealing with such a large graph. A novel metric for measuring the importance of edges in a graph is introduced in this paper. Compared with the other six popular matrices with respect to a number of real-world graphs, the proposed metric is capable of capturing the structural feature of a graph in a scalable way. The comprehensive experiments have demonstrated the performance of the proposed metric. According to this metric, a filtering approach is presented to reduce the visual clutter of a layout in a way that the hidden patterns can be revealed gradually. As evidenced by real-world examples, our approach allows users to explore graphs in real-time with a high level of details in an interactive way in order to gain insight into graph data.

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http://hdl.handle.net/10453/137393