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Network effects on scientific collaborations.

Uddin S, Hossain L, Rasmussen K - PLoS ONE (2013)

Bottom Line: However, little is known about what network properties are associated with authors who have increased number of joint publications and are being cited highly.Also, we reveal that degree centrality and betweenness centrality values of authors in a co-authorship network are positively correlated with the strength of their scientific collaborations.Authors' network positions in co-authorship networks influence the performance (i.e., citation count) and formation (i.e., tie strength) of scientific collaborations.

View Article: PubMed Central - PubMed

Affiliation: Project Management Program and Centre for Complex Systems Research, The University of Sydney, Sydney, Australia. shahadat.uddin@sydney.edu.au

ABSTRACT

Background: The analysis of co-authorship network aims at exploring the impact of network structure on the outcome of scientific collaborations and research publications. However, little is known about what network properties are associated with authors who have increased number of joint publications and are being cited highly.

Methodology/principal findings: Measures of social network analysis, for example network centrality and tie strength, have been utilized extensively in current co-authorship literature to explore different behavioural patterns of co-authorship networks. Using three SNA measures (i.e., degree centrality, closeness centrality and betweenness centrality), we explore scientific collaboration networks to understand factors influencing performance (i.e., citation count) and formation (tie strength between authors) of such networks. A citation count is the number of times an article is cited by other articles. We use co-authorship dataset of the research field of 'steel structure' for the year 2005 to 2009. To measure the strength of scientific collaboration between two authors, we consider the number of articles co-authored by them. In this study, we examine how citation count of a scientific publication is influenced by different centrality measures of its co-author(s) in a co-authorship network. We further analyze the impact of the network positions of authors on the strength of their scientific collaborations. We use both correlation and regression methods for data analysis leading to statistical validation. We identify that citation count of a research article is positively correlated with the degree centrality and betweenness centrality values of its co-author(s). Also, we reveal that degree centrality and betweenness centrality values of authors in a co-authorship network are positively correlated with the strength of their scientific collaborations.

Conclusions/significance: Authors' network positions in co-authorship networks influence the performance (i.e., citation count) and formation (i.e., tie strength) of scientific collaborations.

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Research analysis process.
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pone-0057546-g003: Research analysis process.

Mentions: Using co-authorship dataset, we first construct co-authorship networks for the two research groups of NUS and Monash University. We then quantify network measures (i.e., degree centrality, closeness centrality and betweenness centrality) for each author of those co-authorship networks. We use ORA, which is a dynamic network analysis tool capable of performing node-level and network-level analyses of weighted networks [37], to measure these three network centrality values for each author. Degree centrality and betweenness centrality values of all co-authors are averaged respectively for each paper so that a single degree and betweenness value will be associated with each paper. For measuring tie strength between two authors, we consider the number of scientific papers co-authored by those two authors. Finally, we use the Spearman correlation test to check whether network measures of authors have any impact on citation counts, and on their strength of scientific collaborations. The Spearman correlation test approach is chosen because we notice that the distributions of all network measures considered in this research are non-normal. After that, we use the regression method to explore the impact of SNA measures on the citation count of papers and tie strength between authors. Figure 3 illustrates the flow chart of research analysis process followed in this study.


Network effects on scientific collaborations.

Uddin S, Hossain L, Rasmussen K - PLoS ONE (2013)

Research analysis process.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3585377&req=5

pone-0057546-g003: Research analysis process.
Mentions: Using co-authorship dataset, we first construct co-authorship networks for the two research groups of NUS and Monash University. We then quantify network measures (i.e., degree centrality, closeness centrality and betweenness centrality) for each author of those co-authorship networks. We use ORA, which is a dynamic network analysis tool capable of performing node-level and network-level analyses of weighted networks [37], to measure these three network centrality values for each author. Degree centrality and betweenness centrality values of all co-authors are averaged respectively for each paper so that a single degree and betweenness value will be associated with each paper. For measuring tie strength between two authors, we consider the number of scientific papers co-authored by those two authors. Finally, we use the Spearman correlation test to check whether network measures of authors have any impact on citation counts, and on their strength of scientific collaborations. The Spearman correlation test approach is chosen because we notice that the distributions of all network measures considered in this research are non-normal. After that, we use the regression method to explore the impact of SNA measures on the citation count of papers and tie strength between authors. Figure 3 illustrates the flow chart of research analysis process followed in this study.

Bottom Line: However, little is known about what network properties are associated with authors who have increased number of joint publications and are being cited highly.Also, we reveal that degree centrality and betweenness centrality values of authors in a co-authorship network are positively correlated with the strength of their scientific collaborations.Authors' network positions in co-authorship networks influence the performance (i.e., citation count) and formation (i.e., tie strength) of scientific collaborations.

View Article: PubMed Central - PubMed

Affiliation: Project Management Program and Centre for Complex Systems Research, The University of Sydney, Sydney, Australia. shahadat.uddin@sydney.edu.au

ABSTRACT

Background: The analysis of co-authorship network aims at exploring the impact of network structure on the outcome of scientific collaborations and research publications. However, little is known about what network properties are associated with authors who have increased number of joint publications and are being cited highly.

Methodology/principal findings: Measures of social network analysis, for example network centrality and tie strength, have been utilized extensively in current co-authorship literature to explore different behavioural patterns of co-authorship networks. Using three SNA measures (i.e., degree centrality, closeness centrality and betweenness centrality), we explore scientific collaboration networks to understand factors influencing performance (i.e., citation count) and formation (tie strength between authors) of such networks. A citation count is the number of times an article is cited by other articles. We use co-authorship dataset of the research field of 'steel structure' for the year 2005 to 2009. To measure the strength of scientific collaboration between two authors, we consider the number of articles co-authored by them. In this study, we examine how citation count of a scientific publication is influenced by different centrality measures of its co-author(s) in a co-authorship network. We further analyze the impact of the network positions of authors on the strength of their scientific collaborations. We use both correlation and regression methods for data analysis leading to statistical validation. We identify that citation count of a research article is positively correlated with the degree centrality and betweenness centrality values of its co-author(s). Also, we reveal that degree centrality and betweenness centrality values of authors in a co-authorship network are positively correlated with the strength of their scientific collaborations.

Conclusions/significance: Authors' network positions in co-authorship networks influence the performance (i.e., citation count) and formation (i.e., tie strength) of scientific collaborations.

Show MeSH
Related in: MedlinePlus