Evolution and structure of scientific co-publishing network in Korea between 1948–2011

Kim, Jinseok; Tao, Liang; Lee, Seok-Hyoung; Diesner, Jana

doi:10.1007/s11192-016-1878-5

Cited by 11 publications

(6 citation statements)

References 37 publications

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“…Co-authorship analysis is an approach to weight the countries based on their co-authoring in publication and demonstrate them in a network structure [27]. In previous co-authoring network studies, the Gini index was used as a measure of the distribution in productivity and network structure [28][29][30]. In our study, the Gini index is here applied to calculate the equilibrium degree in international collaborative research.…”

Section: Methodsmentioning

confidence: 99%

The Evolution of International Scientific Collaboration in Fuel Cells during 1998–2017: A Social Network Perspective

2018

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International scientific collaboration has played an important role in the development of fuel cell technology. In this paper, we employ bibliometric methods and social network analysis to explore the patterns and dynamics of scientific collaboration network of fuel cells. A total of 20,358 international collaborative publications in the fuel cell field published during 1998–2017 were collected from Web of Science. We use a series of indicators to address multiple facets of research collaboration and evolution patterns. Results show that international collaboration has been increasing and the characteristics of the scientific network have changed over time. The collaboration network presented a highly uneven distribution, while the sign of decline began to show. The trend of consolidation was presented with one cluster around North America–Asia, one around Europe, and a small emerging collaborating cluster around West Asia. European and North American countries had relatively higher international collaboration rate than Asian countries but lower publishing volume. Two modes of international collaboration exist: Germany, France and UK collaborate with a wide range of countries, while Singapore, Australia, South Korea and Taiwan concentrate on collaborating with few main countries. Microbial fuel cell had developed as a new prominent area in the international collaboration, and the most popular catalysts were nanoparticle and graphene/carbon nanotubes. This study presents a picture of international collaboration from multi-dimension view and provides insights in facilitating more vigorous collaborations in fuel cells.

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Section: Methodsmentioning

confidence: 99%

The Evolution of International Scientific Collaboration in Fuel Cells during 1998–2017: A Social Network Perspective

2018

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“…In the application of the curriculum model, it is not possible to make absolute and simplistic, but also to consider relevant factors such as curriculum objectives, youth development level, subject nature, training conditions, and teacher possibility, so as to achieve the expected results. Regardless of the curriculum model adopted, copying and imitation are not advocated [18,19]. Curriculum has a model, but there is no fixed model.…”

Section: Advocate Diversification Of Sports Curriculum Modelsmentioning

confidence: 99%

Empirical Study on Theories and Techniques of Adolescent Physical Health Promotion under the Background of Big Data

Xu¹

2021

Mobile Information Systems

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In order to make my country’s youth health management more scientific, more standardized, and more effective, it is necessary to conduct in-depth research on the management of youth physical health promotion. Through the investigation and analysis of the physical health data of adolescents in my country, this paper proposes that the results of health intervention training as part of the empirical research on the construction of adolescent health big data management service system can effectively improve the relationship hypothesis of the physical health of adolescents and by selecting the example of our country’s “Adolescent Physical Health Data Analysis in 2020” for regression analysis. The research results show that the theory of adolescent physical health promotion can improve the physical health of adolescents by interfering with students’ physical exercise. In the processing of data, GBDT is suitable when the training set is relatively large, and as the sample size increases, the accuracy rate can reach 79.79%. In terms of the classification accuracy of male sitting forward bending promotion, the accuracy of the RF method is higher than that of GBDT. In terms of the promotion classification effect of boys’ 1000 m running, the RF method achieved the highest promotion accuracy rate of 77.62%. In the male pull-ups to promote the classification effect, when the proportion of the training set is 60%, the RF method gets the highest accuracy rate, which is 92.04%. The results of the classification effect for girls standing long jump promotion show that the classification accuracy rate for girls standing long jump promotion is between 51% and 56%. When the training set is less than 60%, the RF method is the best, the highest is 53.93%, and the rest is the GBDT method, the highest is 55.46%; in Macro-F1, the RF and GBDT indicators have their own advantages. In the promotion of the classification effect on the final fitness level of girls, the accuracy rates of RF and GBDT methods range from 90% to 96%, and the accuracy rates of the NN method range from 80% to 87%; when the practice rate reaches 80%, the GBDT method achieves the highest accuracy rate of 95.06%; on the Macro-F1 index, the GBDT method is obviously the best.

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“…One critical feature of scholarly publication data is the accuracy of entity resolution in terms of splitting up author names that are shared by multiple people into as many nodes and merging variations in referring to individuals (Fegley & Torvik, 2013;. Our original DBLP and KISTI are already disambiguated (for details on KISTI, see Kim, Tao, Lee, & Diesner, 2016; for details on DBLP, see Reitz & Hoffmann, 2011). For MEDLINE and APS, we obtained algorithmically disambiguated data as outlined below.…”

Section: Datamentioning

confidence: 99%

Formational bounds of link prediction in collaboration networks

Kim

Diesner

2019

Scientometrics

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Link prediction in collaboration networks is often solved by identifying structural properties of existing nodes that are disconnected at one point in time, and that share a link later on. The maximally possible recall rate or upper bound of this approach's success is capped by the proportion of links that are formed among existing nodes embedded in these properties. Consequentially, sustained links as well as links that involve one or two new network participants are typically not predicted. The purpose of this study is to highlight formational constraints that need to be considered to increase the practical value of link prediction methods targeted for collaboration networks. In this study, we identify the distribution of basic link formation types based on four large-scale, over-time collaboration networks, showing that roughly speaking, 25% of links represent continued collaborations, 25% of links are new collaborations between existing authors, and 50% are formed between an existing author and a new network member. This implies that for collaboration networks, increasing the accuracy of computational link prediction solutions may not be a reasonable goal when the ratio of collaboration links that are eligible to the classic link prediction process is low.

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Evolution and structure of scientific co-publishing network in Korea between 1948–2011

Cited by 11 publications

References 37 publications

The Evolution of International Scientific Collaboration in Fuel Cells during 1998–2017: A Social Network Perspective

The Evolution of International Scientific Collaboration in Fuel Cells during 1998–2017: A Social Network Perspective

Empirical Study on Theories and Techniques of Adolescent Physical Health Promotion under the Background of Big Data

Formational bounds of link prediction in collaboration networks

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