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

Khan, Gohar Feroz; Moon, Junghoon; Park, Han Woo

doi:10.1007/s11192-011-0489-4

Cited by 7 publications

(4 citation statements)

References 44 publications

(71 reference statements)

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“…Table II shows the network statistics and parameters of the largest connected component. Density is approximately 0.26 at a relatively low level (Khan et al , 2011), which indicates a lower degree of cross-regional collaboration in China. In other words, research collaboration among regions in China is not close enough, and it has not yet matured and become stable in the cross-regional view.…”

Section: Resultsmentioning

confidence: 99%

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Geographical visualization of research collaborations of library science in China

Huang

Wang

2018

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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.

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Section: Resultsmentioning

confidence: 99%

“…The clustering coefficient of this cross-regional collaboration network, at a relatively high level (Khan et al , 2011), means that two regions are much more likely to collaborate if they have a third collaborative region in common. These regions are also more likely to be clustered into one community.…”

Section: Resultsmentioning

confidence: 99%

Geographical visualization of research collaborations of library science in China

Huang

Wang

2018

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“…Several nations would play an important bridging role that connects other nations in research collaborations. Furthermore, the clustering coefficient of this international collaboration network, at a relatively high level (Khan et al, 2011), means that two nations are much more likely to have collaborated if they share a third collaborative nation. This finding is reinforced by the result of five clearly distinguished groups.…”

Section: Resultsmentioning

confidence: 99%

Structure and patterns of cross-national Big Data research collaborations

Zhang

2017

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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.

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“…As noted above, scientometric projects seek to identify a discipline, to structure the information available and to highlight potential areas for future research [27]. In this regard, the analysis of the most commonly published questions in the field of social media could inform researchers of the state of the art and highlight research gaps.…”

Section: Introductionmentioning

confidence: 99%

Theoretical Support for Social Media Research. A Scientometric Analysis

Muñoz

Bolívar

2015

Lecture Notes in Computer Science

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Part 1: FoundationsInternational audienceWe seek to analyze the research into the field of social media implementation in public administrations, examining the theories that support the use of social media, the main use of this technology for public administrations and the trends and research innovations in this area in the future. In addition, a comparative study will be performed in order to identify differences of research, research gaps and interest on the different domains of this topic into different contexts. To achieve the aims, the scientometric methodology will be applied to a sample of papers published in journals listed in the fields of Public Administration, Information Sciences and Communication. In this sense, the research about social media has not been the same in all areas. Knowledge gaps and research opportunities are identified from these observations, which reveal changes in the research methods applied, reinforcing the development of a theoretical framework so that the application of social media may efficiently contribute to improving management in the public sector

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

Cited by 7 publications

References 44 publications

Geographical visualization of research collaborations of library science in China

Geographical visualization of research collaborations of library science in China

Structure and patterns of cross-national Big Data research collaborations

Theoretical Support for Social Media Research. A Scientometric Analysis

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