Limits...
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.

Show MeSH

Related in: MedlinePlus

Network attributes for each author and the corresponding citation count of the paper.Three basic centrality measures (i.e., degree centrality, closeness centrality and betweenness centrality) are considered. CRD stands for ‘Complete Research Dataset’.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3585377&req=5

pone-0057546-g004: Network attributes for each author and the corresponding citation count of the paper.Three basic centrality measures (i.e., degree centrality, closeness centrality and betweenness centrality) are considered. CRD stands for ‘Complete Research Dataset’.

Mentions: We plot the citation count of each paper against the network attribute of each of its all co-authors in Figure 4. This figure illustrates how the research dataset look like in terms of network position of each co-author and the corresponding citation count of the paper. We considered degree centrality, closeness centrality and betweenness centrality to measure network position of each co-author. A significant difference in citation counts of published papers (for the complete research dataset as well as for both NUS and Monus University groups) is noticed for authors who have same values for network measures. This could be explained by the fact that there are few highly connected and well cited authors (e.g., professor) in all three networks and less prominent authors (i.e., less connected and less cited) have co-authored with them (e.g., student-professor link). It is also noticed that betweenness centrality values for many authors are zero. These authors could be either students and/or new comers to the scientific field. For this reason, they do not play any bridging role in the co-authorship network.


Network effects on scientific collaborations.

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

Network attributes for each author and the corresponding citation count of the paper.Three basic centrality measures (i.e., degree centrality, closeness centrality and betweenness centrality) are considered. CRD stands for ‘Complete Research Dataset’.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0057546-g004: Network attributes for each author and the corresponding citation count of the paper.Three basic centrality measures (i.e., degree centrality, closeness centrality and betweenness centrality) are considered. CRD stands for ‘Complete Research Dataset’.
Mentions: We plot the citation count of each paper against the network attribute of each of its all co-authors in Figure 4. This figure illustrates how the research dataset look like in terms of network position of each co-author and the corresponding citation count of the paper. We considered degree centrality, closeness centrality and betweenness centrality to measure network position of each co-author. A significant difference in citation counts of published papers (for the complete research dataset as well as for both NUS and Monus University groups) is noticed for authors who have same values for network measures. This could be explained by the fact that there are few highly connected and well cited authors (e.g., professor) in all three networks and less prominent authors (i.e., less connected and less cited) have co-authored with them (e.g., student-professor link). It is also noticed that betweenness centrality values for many authors are zero. These authors could be either students and/or new comers to the scientific field. For this reason, they do not play any bridging role in the co-authorship network.

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