Proving ground for social network analysis in the emerging research area “Internet of Things” (IoT)

Arif Mehmood (), Gyu Sang Choi (), Otto F. Feigenblatt and Han Woo Park ()
Additional contact information
Arif Mehmood: Yeungnam University
Gyu Sang Choi: Yeungnam University
Otto F. Feigenblatt: Royal Academy of Doctors of Spain
Han Woo Park: Yeungnam University

Scientometrics, 2016, vol. 109, issue 1, No 10, 185-201

Abstract: Abstract This study examines the structural patterns of international co-institutions and co-authors in science citation index papers in the research domain of the Internet of Things (IoT). The study uses measures from the social network analysis method, including degree centrality, betweenness centrality, eigenvector centrality, and effectiveness, to investigate the effects of social networks. In addition, the study proposes a prediction model for assessing the semantic relevancy of research papers in the field of IoT (Regarding social science approach on semantic analysis, refer to Jung and Park in Gov Inf Q, 2015a. doi: 10.1016/j.giq.2015.09.010 , 32(3):353–358, 2015b). For the analysis, 815 research papers were selected from the Web of Science database for the 1993–2015 period. Empirical analysis results identify China as the most central country, followed by the U.S., Spain, the U.K., and Sweden, in terms of the co-authored network. Similarly, the Chinese Academy of Sciences, the Beijing University of Posts and Telecommunications, and Shanghai Jiao Tong University were ranked first, third, and fourth, respectively, among the top five co-institutions. Northeastern University (U.S.) and the University of Surrey (U.K.) ranked second and fifth, respectively. A confusion matrix was used to validate the accuracy of the proposed model. The accuracy of the prediction model was 76.84 %, whereas recall for the model (ability of a search to find all relevant items in the corpus) was 94.47 %.

Keywords: Data mining; Social network analysis; Internet of Things (IoT); Co-authorship; Co-institutions; Prediction model (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s11192-016-1931-4

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