An integrated examination of collaboration coauthorship networks through structural cohesion, holes, hierarchy, and percolating clusters

2014 ◽  
Vol 65 (8) ◽  
pp. 1639-1661 ◽  
Author(s):  
Jaideep Ghosh ◽  
Avinash Kshitij
Keyword(s):  
Coauthorship Networks ◽  
Structural Cohesion

4. Paths and Semipaths: Reconceptualizing Structural Cohesion in Terms of Directed Relations

2009 ◽  
Vol 39 (1) ◽  
pp. 117-150 ◽  
Author(s):  
Rick Grannis
Keyword(s):  
Social Relations ◽  
Social Life ◽  
Graduate Programs ◽  
Data Sets ◽  
Factor I ◽  
Structural Cohesion

In a groundbreaking article, Moody and White (2003) introduced the concept of structural cohesion, simultaneously characterizing emergent communities and their internally embedded layers by the number of node-independent paths interconnecting individuals. Like many studies, however, they “corrected” the directionality discovered in some of their data. While often done for important purposes, doing so potentially confounds structural cohesion with unrelated concepts. Some relations, especially those relating to the dynamic aspects of social life, are inherently directed, in whole or in part, and it may prove worthwhile to respect this directionality. In this article, I recast structural cohesion in terms of directed social relations and identify four distinct ways of measuring it. In two example data sets—hiring relations among graduate programs and trust relations among neighborhood residents—I show that only strong embeddedness, a type of structural cohesion emerging from directed relations, proves to be a powerful, robust, independent explanatory factor. I further show that if the directionality in the data in these examples had been “corrected,” the importance of structural cohesion would have been dramatically undervalued.

scholarly journals Clique Approach for Networks: Applications for Coauthorship Networks

2014 ◽  
Vol 03 (02) ◽  
pp. 80-85 ◽  
Author(s):  
Marcos Grilo Rosa ◽  
Inácio de Sousa Fadigas ◽  
Maria Teresinha Tamanini Andrade ◽  
Hernane Borges de Barros Pereira
Keyword(s):  
Coauthorship Networks

Gender inequities in the online dissemination of scholars’ work

2021 ◽  
Vol 118 (39) ◽  
pp. e2102945118
Author(s):  
Orsolya Vásárhelyi ◽  
Igor Zakhlebin ◽  
Staša Milojević ◽  
Emőke-Ágnes Horvát
Keyword(s):  
Gender Gap ◽  
Large Scale ◽  
Gender Disparities ◽  
Female Representation ◽  
Scientific Impact ◽  
The Public ◽  
Research Areas ◽  
Coauthorship Networks ◽  
The Impact ◽  
The Relationship

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.

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

2018 ◽  
Vol 16 (2) ◽  
pp. 65-79 ◽  
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.

Structural cohesion and embeddedness in two-mode networks

2019 ◽  
Vol 43 (4) ◽  
pp. 179-194 ◽  
Author(s):  
Benjamin Cornwell ◽  
Jake Burchard
Keyword(s):  
Structural Cohesion

scholarly journals Modelling transition phenomena of scientific coauthorship networks

2017 ◽  
Vol 69 (2) ◽  
pp. 305-317 ◽  
Author(s):  
Z. Xie ◽  
Z. Ouyang ◽  
J. Li ◽  
E. Dong ◽  
D. Yi
Keyword(s):  
Coauthorship Networks

Clustering-based link prediction in scientific coauthorship networks

2017 ◽  
Vol 28 (06) ◽  
pp. 1750082 ◽  
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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