Hierarchical decomposition of big graphs

Zhang, Y; Qin, L; Zhang, F; Zhang, W

Hierarchical decomposition of big graphs

Zhang, Y

Qin, L

Zhang, F Zhang, W

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Publication Type:: Conference Proceeding
Citation:: Proceedings - International Conference on Data Engineering, 2019, 2019-April pp. 2064 - 2067
Issue Date:: 2019-04-01

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Field	Value	Language
dc.contributor.author	Zhang, Y https://orcid.org/0000-0002-2674-1638	en_US
dc.contributor.author	Qin, L https://orcid.org/0000-0001-6068-5062	en_US
dc.contributor.author	Zhang, F	en_US
dc.contributor.author	Zhang, W	en_US
dc.date.issued	2019-04-01	en_US
dc.identifier.citation	Proceedings - International Conference on Data Engineering, 2019, 2019-April pp. 2064 - 2067	en_US
dc.identifier.isbn	9781538674741	en_US
dc.identifier.issn	1084-4627	en_US
dc.identifier.uri	http://hdl.handle.net/10453/136282
dc.description.abstract	© 2019 IEEE. Graph decomposition has been widely used to analyze real-life networks from different perspectives. Recent studies focus on the hierarchical graph decomposition methods to handle big graphs in many real-life applications such as community detection, network analysis, network visualization, internet topology analysis and protein function prediction. In this tutorial, we first highlight the importance of hierarchical graph decomposition in a variety of applications and the unique challenges that need to be addressed. Subsequently, we provide an overview of the existing models and the computation algorithms under different computing environments. Then we discuss the integration of existing models with other approaches to better capture the cohesiveness of subgraphs in real-life scenarios. Finally, we discuss the future research directions in this important and growing research area.	en_US
dc.relation	http://purl.org/au-research/grants/arc/FT170100128
dc.relation	http://purl.org/au-research/grants/arc/DP180103096
dc.relation.ispartof	Proceedings - International Conference on Data Engineering	en_US
dc.relation.isbasedon	10.1109/ICDE.2019.00240	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Hierarchical decomposition of big graphs	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2019-April	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 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 - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US
pubs.volume	2019-April	en_US

Abstract:

© 2019 IEEE. Graph decomposition has been widely used to analyze real-life networks from different perspectives. Recent studies focus on the hierarchical graph decomposition methods to handle big graphs in many real-life applications such as community detection, network analysis, network visualization, internet topology analysis and protein function prediction. In this tutorial, we first highlight the importance of hierarchical graph decomposition in a variety of applications and the unique challenges that need to be addressed. Subsequently, we provide an overview of the existing models and the computation algorithms under different computing environments. Then we discuss the integration of existing models with other approaches to better capture the cohesiveness of subgraphs in real-life scenarios. Finally, we discuss the future research directions in this important and growing research area.

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http://hdl.handle.net/10453/136282