Structure and patterns of cross-national Big Data research collaborations

Abstract

Purpose The purpose of this paper is to reveal the structure and patterns of cross-national collaborations in Big Data research through application of various social network analysis and geographical visualization methods. Design/methodology/approach The sample includes articles containing Big Data research, covering all years, in the Web of Science Core Collection as of December 2015. First, co-occurrence data representing collaborations among nations were extracted from author affiliations. Second, the descriptive statistics, network indicators of collaborations, and research communities were calculated. Third, topological network maps, geographical maps integrated with topological network projections, and proportional maps were produced for visualization. Findings The results show that the scope of international collaborations in Big Data research is broad, but the distribution among nations is unbalanced and fragmented. The USA, China, and the UK were identified as the major contributors to this research area. Five research communities are identified, led by the USA, China, Italy, South Korea, and Brazil. Collaborations within each community vary, reflecting different levels of research development. The visualizations show that nations advance in Big Data research are centralized in North America, Europe, and Asia-Pacific. Originality/value This study applied various informetric methods and tools to reveal the collaboration structure and patterns among nations in Big Data research. Visualized maps help shed new light on global research efforts.