Network of the core: mapping and visualizing the core of scientific domains

Geographical visualization of research collaborations of library science in China

Purpose

The purpose of this paper is to visualize the collaboration network among regions in library science (LS) in China. Using various methods and tools of social network analysis and geographical visualization, results were obtained, showing the structure and patterns of research collaborations in topological and geographical views, as well as the geographical distributions of contribution.

Design/methodology/approach

The sample includes all studies published in the top journal in library science in China from 2006 to 2015. First, co-occurrence data representing collaborations among regions was extracted from author affiliations. Second, the topological network of collaboration was generated by applying social network analysis tools and descriptive statistics, network indicators of the collaboration network and research communities were calculated. Third, the topological network was projected into a geographical map with corresponding coordinates and distances using geographical tools. Finally, the topological network maps and the geographical maps were produced for visualization.

Findings

The levels of contribution are very unbalanced between regions, and overall research collaboration is low. Beijing, Hubei and several other areas are central and significant regions in China; other regions are mostly connected with central ones through direct collaborations. Research collaborations in LS research in China are mostly distributed in the east and south of China, being centralized in the “Beijing–Hubei–Shanghai” triangle zone, as well as within the triangle’s extending zones. Finally, there are three distinct research communities that connect closely within themselves and loosely between them. The Beijing community is relatively centralized in geography, while other communities are scattered.

Originality/value

This study applied various methods and tools of social network analysis and geographical mapping analysis to reveal the collaboration structure and patterns among regions in LS research in China. Visualized maps in topological and geographical views help shed new light on research efforts.

Structure and patterns of cross-national Big Data research collaborations

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.