The coauthorship networks of the most productive European researchers

Unbiased science dissemination has the potential to alleviate some of the known gender disparities in academia by exposing female scholars’ work to other scientists and the public. And yet, we lack comprehensive understanding of the relationship between gender and science dissemination online. Our large-scale analyses, encompassing half a million scholars, revealed that female scholars’ work is mentioned less frequently than male scholars’ work in all research areas. When exploring the characteristics associated with online success, we found that the impact of prior work, social capital, and gendered tie formation in coauthorship networks are linked with online success for men, but not for women—even in the areas with the highest female representation. These results suggest that while men’s scientific impact and collaboration networks are associated with higher visibility online, there are no universally identifiable facets associated with success for women. Our comprehensive empirical evidence indicates that the gender gap in online science dissemination is coupled with a lack of understanding the characteristics that are linked with female scholars’ success, which might hinder efforts to close the gender gap in visibility.

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An Analysis of the Structure and Evolution of the Distance Education Research Area Community in Terms of Coauthorships

International Journal of Distance Education Technologies ◽

10.4018/ijdet.2018040105 ◽

2018 ◽

Vol 16 (2) ◽

pp. 65-79 ◽

Cited By ~ 2

Author(s):

Rodrigo Richard Gomes ◽

Marcelo Werneck Barbosa

Keyword(s):

Distance Education ◽

Education Research ◽

Special Kind ◽

Research Area ◽

Research Community ◽

Topological Properties ◽

Coauthorship Networks ◽

Centrality Metrics ◽

Number Of Publications ◽

Near Future

This article aims at characterizing the research community of Distance Education (DE) with respect to coauthorship, a special kind of collaboration among researchers, according to publications of 11 relevant DE journals. This article identified who the central researchers are, the topological properties of the coauthorship networks analyzed, the coauthorship patterns of each journal and the evolution of the DE community in the last 30 years. In order to achieve these goals, Social Network Analysis (SNA) was used, deriving centrality metrics, which depict the importance of researchers in the networks. This study found out that researchers who publish more papers are not necessarily the ones considered more central according to SNA. Besides, promising researchers, those linked to central researchers and are considered more likely to coauthor papers in the near future. Different coauthorship patterns among journals are described. Finally, a steep increase in the number of publications and coauthorships in the last decades was observed.

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A cooperative game model for the multimodality of coauthorship networks

Scientometrics ◽

10.1007/s11192-019-03183-z ◽

2019 ◽

Vol 121 (1) ◽

pp. 503-519 ◽

Cited By ~ 3

Author(s):

Zheng Xie

Keyword(s):

Cooperative Game ◽

Game Model ◽

Coauthorship Networks ◽

Cooperative Game Model

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Over-time measurement of triadic closure in coauthorship networks

Social Network Analysis and Mining ◽

10.1007/s13278-017-0428-3 ◽

2017 ◽

Vol 7 (1) ◽

Cited By ~ 6

Author(s):

Jinseok Kim ◽

Jana Diesner

Keyword(s):

Time Measurement ◽

Coauthorship Networks ◽

Triadic Closure ◽

Over Time

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Modelling transition phenomena of scientific coauthorship networks

Journal of the Association for Information Science and Technology ◽

10.1002/asi.23935 ◽

2017 ◽

Vol 69 (2) ◽

pp. 305-317 ◽

Cited By ~ 12

Author(s):

Z. Xie ◽

Z. Ouyang ◽

J. Li ◽

E. Dong ◽

D. Yi

Keyword(s):

Coauthorship Networks

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Clustering-based link prediction in scientific coauthorship networks

International Journal of Modern Physics C ◽

10.1142/s0129183117500826 ◽

2017 ◽

Vol 28 (06) ◽

pp. 1750082 ◽

Cited By ~ 1

Author(s):

Yang Ma ◽

Guangquan Cheng ◽

Zhong Liu ◽

Xingxing Liang

Keyword(s):

Social Networks ◽

Link Prediction ◽

Clustering Method ◽

Scientific Cooperation ◽

Coauthorship Networks ◽

Cooperation Network ◽

Better Than

Link prediction in social networks has become a growing concern among researchers. In this paper, the clustering method was used to exploit the grouping tendency of nodes, and a clustering index (CI) was proposed to predict potential links with characteristics of scientific cooperation network taken into consideration. Results showed that CI performed better than the traditional indices for scientific coauthorship networks by compensating for their disadvantages. Compared with traditional algorithms, this method for a specific type of network can better reflect the features of the network and achieve more accurate predictions.

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