Using field of research codes to discover research groups from co-authorship networks

Meng, Q; Kennedy, PJ

Using field of research codes to discover research groups from co-authorship networks

Meng, Q Kennedy, PJ

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012, 2012, pp. 289 - 293
Issue Date:: 2012-12-01

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Field	Value	Language
dc.contributor.author	Meng, Q	en_US
dc.contributor.author	Kennedy, PJ https://orcid.org/0000-0001-7837-3171	en_US
dc.date.issued	2012-12-01	en_US
dc.identifier.citation	Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012, 2012, pp. 289 - 293	en_US
dc.identifier.isbn	9780769547992	en_US
dc.identifier.uri	http://hdl.handle.net/10453/22279
dc.description.abstract	Nowadays, academic collaboration has become more prevalent and crucial than ever before and many studies of academic collaboration analysis are implemented based on coauthorship networks. This paper aims to build a novel coauthorship network by importing field of research codes based on Newman's model, and then analyze and extract research groups via spectral clustering. In order to explain the effectiveness of this revised network, we take the academic collaboration at the University of Technology, Sydney (UTS) as an example. The result of this study advances methods for selecting the most prolific research groups and individuals in research institutions, and provides scientific evidence for policymakers to manage laboratories and research groups more efficiently in the future. © 2012 IEEE.	en_US
dc.relation.ispartof	Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012	en_US
dc.relation.isbasedon	10.1109/ASONAM.2012.56	en_US
dc.title	Using field of research codes to discover research groups from co-authorship networks	en_US
dc.type	Conference Proceeding
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Istanbul, Turkey	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
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Nowadays, academic collaboration has become more prevalent and crucial than ever before and many studies of academic collaboration analysis are implemented based on coauthorship networks. This paper aims to build a novel coauthorship network by importing field of research codes based on Newman's model, and then analyze and extract research groups via spectral clustering. In order to explain the effectiveness of this revised network, we take the academic collaboration at the University of Technology, Sydney (UTS) as an example. The result of this study advances methods for selecting the most prolific research groups and individuals in research institutions, and provides scientific evidence for policymakers to manage laboratories and research groups more efficiently in the future. © 2012 IEEE.

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http://hdl.handle.net/10453/22279