Discovering the interdisciplinary nature of Big Data research through social network analysis and visualization
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Big Data is a research field involving a large number of collaborating disciplines. Based on bibliometric data downloaded from the Web of Science, this study applies various social network analysis and visualization tools to examine the structure and patterns of interdisciplinary collaborations, as well as the recently evolving overall pattern. This study presents the descriptive statistics of disciplines involved in publishing Big Data research; and network indicators of the interdisciplinary collaborations among disciplines, interdisciplinary communities, interdisciplinary networks, and changes in discipline communities over time. The findings indicate that the scope of disciplines involved in Big Data research is broad, but that the disciplinary distribution is unbalanced. The overall collaboration among disciplines tends to be concentrated in several key fields. According to the network indicators, Computer Science, Engineering, and Business and Economics are the most important contributors to Big Data research, given their position and role in the research collaboration network. Centering around a few important disciplines, all fields related to Big Data research are aggregated into communities, suggesting some related research areas, and directions for Big Data research. An ever-changing roster of related disciplines provides support, as illustrated by the evolving graph of communities.
KeywordsBig Data research Interdisciplinary collaboration Network structure and patterns Visualization
This study is supported by China Postdoctoral Science Foundation Special Funded Project (No. 2016T90736), China Postdoctoral Science Foundation Funded Project (No. 2015M572202), Wuhan University Initiative Scientific Research Project (No. 2015-79), National Natural Science Foundation of China Funded Project (No. 71303178), and Kent State University 2014 Postdoctoral Program for the Smart Big Data project.
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