Detecting Hotspots in Interdisciplinary Research Based on Overlapping Community Detection

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...

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Published in2018 Portland International Conference on Management of Engineering and Technology (PICMET) pp. 1 - 6
Main Authors Huang, Lu, Jia, Xiang, Zhang, Yi, Zhou, Xiao, Zhu, Yihe
Format Conference Proceeding
LanguageEnglish
Published Portland International Conference on Management of Engineering and Technology, Inc. (PICMET) 01.08.2018
Subjects
Big Data
Crowdsourcing
Data models
Data visualization
Government
Social network services
Software
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DOI10.23919/PICMET.2018.8481972

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Summary: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.
DOI:10.23919/PICMET.2018.8481972