Detecting hotspots in interdisciplinary research based on overlapping community detection

Huang, L; Jia, X; Zhang, Y; Zhou, X; Zhu, Y

Detecting hotspots in interdisciplinary research based on overlapping community detection

Huang, L Jia, X Zhang, Y

Zhou, X Zhu, Y

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Publication Type:: Conference Proceeding
Citation:: PICMET 2018 - Portland International Conference on Management of Engineering and Technology: Managing Technological Entrepreneurship: The Engine for Economic Growth, Proceedings, 2018
Issue Date:: 2018-10-04

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Field	Value	Language
dc.contributor.author	Huang, L	en_US
dc.contributor.author	Jia, X	en_US
dc.contributor.author	Zhang, Y https://orcid.org/0000-0002-7731-0301	en_US
dc.contributor.author	Zhou, X	en_US
dc.contributor.author	Zhu, Y	en_US
dc.date.issued	2018-10-04	en_US
dc.identifier.citation	PICMET 2018 - Portland International Conference on Management of Engineering and Technology: Managing Technological Entrepreneurship: The Engine for Economic Growth, Proceedings, 2018	en_US
dc.identifier.isbn	9781890843373	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132035
dc.description.abstract	© 2018 Portland International Conference on Management of Engineering and Technology, Inc. (PICMET). Disciplinary fusion has been observed in a range of previous studies, which creates great benefit in science, technology, and innovation. However, how to detect and distinguish the hotspots in interdisciplinary is a challenge for not only researchers but also stakeholders in government and industry sectors. A keywords' co-occurrence network is constructed by using academic articles published between 2012 and 2017 in the field of Information Science and Artificial Intelligence. Then statistical methods are applied for finding the regularities of distributions of term frequency and keywords' k-cliques. Furthermore, the software CFinder, which is based on clique percolation method (CPM) algorithm and is used for detecting overlapping communities, is utilized to visualize the network. At last, we find that 'Social media', 'conceptual model, 'Big Data' and 'Crowdsourcing' are the hotspots of interdisciplinary research in this case.	en_US
dc.relation.ispartof	PICMET 2018 - Portland International Conference on Management of Engineering and Technology: Managing Technological Entrepreneurship: The Engine for Economic Growth, Proceedings	en_US
dc.relation.isbasedon	10.23919/PICMET.2018.8481972	en_US
dc.title	Detecting hotspots in interdisciplinary research based on overlapping community detection	en_US
dc.type	Conference Proceeding
utslib.for	0906 Electrical and Electronic Engineering	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/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2018 Portland International Conference on Management of Engineering and Technology, Inc. (PICMET). Disciplinary fusion has been observed in a range of previous studies, which creates great benefit in science, technology, and innovation. However, how to detect and distinguish the hotspots in interdisciplinary is a challenge for not only researchers but also stakeholders in government and industry sectors. A keywords' co-occurrence network is constructed by using academic articles published between 2012 and 2017 in the field of Information Science and Artificial Intelligence. Then statistical methods are applied for finding the regularities of distributions of term frequency and keywords' k-cliques. Furthermore, the software CFinder, which is based on clique percolation method (CPM) algorithm and is used for detecting overlapping communities, is utilized to visualize the network. At last, we find that 'Social media', 'conceptual model, 'Big Data' and 'Crowdsourcing' are the hotspots of interdisciplinary research in this case.

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

http://hdl.handle.net/10453/131071