Centrality Measures for Bibliometric Network Analysis

이재윤,

doi:10.4275/kslis.2006.40.3.191

Cited by 32 publications

(10 citation statements)

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“…In this study, keywords in areas of interest were extracted and analyzed to identify the flow of the research in these areas. The centrality index, used to analyze social networks, has a disadvantage in that it does not reflect the weights on connections between nodes [ 35 ]. To investigate the centrality of a research subject in this study, the number of connected keywords and the weights depending on the number of connections were reflected.…”

Section: Discussionmentioning

confidence: 99%

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Social Network Analysis of Elders' Health Literacy and their Use of Online Health Information

Jang

2014

Healthc Inform Res

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ObjectivesUtilizing social network analysis, this study aimed to analyze the main keywords in the literature regarding the health literacy of and the use of online health information by aged persons over 65.MethodsMedical Subject Heading keywords were extracted from articles on the PubMed database of the National Library of Medicine. For health literacy, 110 articles out of 361 were initially extracted. Seventy-one keywords out of 1,021 were finally selected after removing repeated keywords and applying pruning. Regarding the use of online health information, 19 articles out of 26 were selected. One hundred forty-four keywords were initially extracted. After removing the repeated keywords, 74 keywords were finally selected.ResultsHealth literacy was found to be strongly connected with 'Health knowledge, attitudes, practices' and 'Patient education as topic.' 'Computer literacy' had strong connections with 'Internet' and 'Attitude towards computers.' 'Computer literacy' was connected to 'Health literacy,' and was studied according to the parameters 'Attitude towards health' and 'Patient education as topic.' The use of online health information was strongly connected with 'Health knowledge, attitudes, practices,' 'Consumer health information,' 'Patient education as topic,' etc. In the network, 'Computer literacy' was connected with 'Health education,' 'Patient satisfaction,' 'Self-efficacy,' 'Attitude to computer,' etc.ConclusionsResearch on older citizens' health literacy and their use of online health information was conducted together with study of computer literacy, patient education, attitude towards health, health education, patient satisfaction, etc. In particular, self-efficacy was noted as an important keyword. Further research should be conducted to identify the effective outcomes of self-efficacy in the area of interest.

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Section: Discussionmentioning

confidence: 99%

“…This study applied the notion of weighted degree centrality to investigate the changes in the main keywords in each section of the networks [ 35 ]. Normally, degree centrality standardization is performed for networks of different sizes.…”

Section: Methodsmentioning

confidence: 99%

Social Network Analysis of Elders' Health Literacy and their Use of Online Health Information

Jang

2014

Healthc Inform Res

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“…The authors analyzed a network of core keywords to understand research trends in the study field. The social network research using centrality measures are not typically weighted [ 44 ]. This study uses weighted measures for linked keywords as well as a number of connections to understand the centrality of research topics.…”

Section: Discussionmentioning

confidence: 99%

“…To compose the social network of selected keywords, this study used NetMiner V.3. A network with weighted degree centrality was constructed [ 44 ]. Keywords are expressed by nodes.…”

Section: Methodsmentioning

confidence: 99%

Application of Social Network Analysis to Health Care Sectors

Jang

Lee

2012

Healthc Inform Res

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ObjectivesThis study aimed to examine the feasibility of social network analysis as a valuable research tool for indicating a change in research topics in health care and medicine.MethodsPapers used in the analysis were collected from the PubMed database at the National Library of Medicine. After limiting the search to papers affiliated with the National Institutes of Health, 27,125 papers were selected for the analysis. From these papers, the top 100 non-duplicate and most studied Medical Subject Heading terms were extracted. NetMiner V.3 was used for analysis. Weighted degree centrality was applied to the analysis to compare the trends in the change of research topics. Changes in the core keywords were observed for the entire group and in three-year intervals.ResultsThe core keyword with the highest centrality value was "Risk Factor," followed by "Molecular Sequence Data," "Neoplasms," "Signal Transduction," "Brain," and "Amino Acid Sequence." Core keywords varied between time intervals, changing from "Molecular Sequence Data" to "Risk Factors" over time. "Risk Factors" was added as a new keyword and its social network was expanded. The slope of the keywords also varied over time: "Molecular Sequence Data," with a high centrality value, had a decreasing slope at certain intervals, whereas "SNP," with a low centrality value, had an increasing slope at certain intervals.ConclusionsThe social network analysis method is useful for tracking changes in research topics over time. Further research should be conducted to confirm the usefulness of this method in health care and medicine.

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“…[14] The nodes in bibliometric networks can represent keywords, authors and institutions, while lines among nodes re ect the density of their relationships. [15] CiteSpace, VOS Viewer and Ucinet are widely-used software packages for bibliometric analysis and visualization. Bibliometric methodology has stimulated the trend of analyzing the top cited one hundred classics retrieved from literature database since the number of citations for an article is a valuable measure of the article's impact on the topic it covers.…”

Section: Introductionmentioning

confidence: 99%

Unicompartmental Knee Arthroplasty Over the Past 40 Years: a Global Analysis and Review of the Most Highly Cited Articles

Jiang

Wei

Zhang

et al. 2021

Preprint

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Background: The aim of this study was to use citation analysis to identify the top 100 papers in unicompartmental knee arthroplasty (UKA) and report the qualities and characteristics of the most influential articles in UKA research.Methods: UKA–related articles published from 1950 to June 2020 were identified using multiple databases. The 100 most cited articles were selected for further analysis of citation count, citation density, current citation rate, authorship, theme, geographic distribution, level of evidence, and network analysis.Results: The top 100 articles' publication dates ranged from 1980 to 2015, with the largest number in the 2000s. Total citations per article ranged from 65 to 473. The USA was the most productive country, but England led the race in terms of citations. The Bone & Joint Journal was the most prolific journal with 25 articles. Price AJ was the most productive first author and corresponding author. The most popular study theme was therapeutic research, followed by prognostic research. The most common level of evidence was level IV. The network analysis connoted that follow-up study had the highest degree of centrality.Conclusions: The study of UKA is steadily evolving. It could be predicted that there will be an increasing number of publications on UKA research in the following years according to the current global trends, the United States and England will likely maintain leadership in this area. Meanwhile, further prospective studies are required to better recognize the prognosis of UKA procedures.

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Centrality Measures for Bibliometric Network Analysis

Cited by 32 publications

References 0 publications

Social Network Analysis of Elders' Health Literacy and their Use of Online Health Information

Social Network Analysis of Elders' Health Literacy and their Use of Online Health Information

Application of Social Network Analysis to Health Care Sectors

Unicompartmental Knee Arthroplasty Over the Past 40 Years: a Global Analysis and Review of the Most Highly Cited Articles

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