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The evolution of your success lies at the centre of your co-authorship network.

Servia-Rodríguez S, Noulas A, Mascolo C, Fernández-Vilas A, Díaz-Redondo RP - PLoS ONE (2015)

Bottom Line: Collaboration among scholars and institutions is progressively becoming essential to the success of research grant procurement and to allow the emergence and evolution of scientific disciplines.Our work focuses on analysing if the volume of collaborations of one author together with the relevance of his collaborators is somewhat related to his research performance over time.To our knowledge, this is the first work that considers success evolution with respect to co-authorship.

View Article: PubMed Central - PubMed

Affiliation: Computer Laboratory, University of Cambridge, Cambridge, United Kingdom; I&C Laboratory, AtlantTIC Research Center, University of Vigo, Vigo, Spain.

ABSTRACT
Collaboration among scholars and institutions is progressively becoming essential to the success of research grant procurement and to allow the emergence and evolution of scientific disciplines. Our work focuses on analysing if the volume of collaborations of one author together with the relevance of his collaborators is somewhat related to his research performance over time. In order to prove this relation we collected the temporal distributions of scholars' publications and citations from the Google Scholar platform and the co-authorship network (of Computer Scientists) underlying the well-known DBLP bibliographic database. By the application of time series clustering, social network analysis and non-parametric statistics, we observe that scholars with similar publications (citations) patterns also tend to have a similar centrality in the co-authorship network. To our knowledge, this is the first work that considers success evolution with respect to co-authorship.

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Time series clustering in Google Scholar and Centrality measures in DBLP.(a) Number of authors attending to the year of their first publication (citation). (b) Number of clusters of authors per year. (c) Number of authors per cluster per year (attending to the temporal evolution of their publications). (d) Number of authors per cluster per year (attending to the temporal evolution of their citations). (e) Degree centrality in the co-authorship network (for authors simultaneously in both datasets and for authors in the entire DBLP). (f) Betweenness centrality in the co-authorship network (for authors simultaneously in both datasets and for authors in the entire DBLP). (g) Closeness centrality in the co-authorship network (for authors simultaneously in both datasets and for authors in the entire DBLP). (h) Eigenvector centrality in the co-authorship network (for authors simultaneously in both datasets and for authors in the entire DBLP).
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pone.0114302.g001: Time series clustering in Google Scholar and Centrality measures in DBLP.(a) Number of authors attending to the year of their first publication (citation). (b) Number of clusters of authors per year. (c) Number of authors per cluster per year (attending to the temporal evolution of their publications). (d) Number of authors per cluster per year (attending to the temporal evolution of their citations). (e) Degree centrality in the co-authorship network (for authors simultaneously in both datasets and for authors in the entire DBLP). (f) Betweenness centrality in the co-authorship network (for authors simultaneously in both datasets and for authors in the entire DBLP). (g) Closeness centrality in the co-authorship network (for authors simultaneously in both datasets and for authors in the entire DBLP). (h) Eigenvector centrality in the co-authorship network (for authors simultaneously in both datasets and for authors in the entire DBLP).

Mentions: With respect to the distribution of authors by year of their first publication (citation), Fig. 1A represents the number of authors classified by (i) the year in which they achieved their first publication and (ii) the year in which any of their publications received its first citation. Both the Computer Science discipline and the Google Scholar platform are relatively recent in comparison to other scientific disciplines and other scientific databases, which could explain why the curves of the number of authors per year are increasing.


The evolution of your success lies at the centre of your co-authorship network.

Servia-Rodríguez S, Noulas A, Mascolo C, Fernández-Vilas A, Díaz-Redondo RP - PLoS ONE (2015)

Time series clustering in Google Scholar and Centrality measures in DBLP.(a) Number of authors attending to the year of their first publication (citation). (b) Number of clusters of authors per year. (c) Number of authors per cluster per year (attending to the temporal evolution of their publications). (d) Number of authors per cluster per year (attending to the temporal evolution of their citations). (e) Degree centrality in the co-authorship network (for authors simultaneously in both datasets and for authors in the entire DBLP). (f) Betweenness centrality in the co-authorship network (for authors simultaneously in both datasets and for authors in the entire DBLP). (g) Closeness centrality in the co-authorship network (for authors simultaneously in both datasets and for authors in the entire DBLP). (h) Eigenvector centrality in the co-authorship network (for authors simultaneously in both datasets and for authors in the entire DBLP).
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4356565&req=5

pone.0114302.g001: Time series clustering in Google Scholar and Centrality measures in DBLP.(a) Number of authors attending to the year of their first publication (citation). (b) Number of clusters of authors per year. (c) Number of authors per cluster per year (attending to the temporal evolution of their publications). (d) Number of authors per cluster per year (attending to the temporal evolution of their citations). (e) Degree centrality in the co-authorship network (for authors simultaneously in both datasets and for authors in the entire DBLP). (f) Betweenness centrality in the co-authorship network (for authors simultaneously in both datasets and for authors in the entire DBLP). (g) Closeness centrality in the co-authorship network (for authors simultaneously in both datasets and for authors in the entire DBLP). (h) Eigenvector centrality in the co-authorship network (for authors simultaneously in both datasets and for authors in the entire DBLP).
Mentions: With respect to the distribution of authors by year of their first publication (citation), Fig. 1A represents the number of authors classified by (i) the year in which they achieved their first publication and (ii) the year in which any of their publications received its first citation. Both the Computer Science discipline and the Google Scholar platform are relatively recent in comparison to other scientific disciplines and other scientific databases, which could explain why the curves of the number of authors per year are increasing.

Bottom Line: Collaboration among scholars and institutions is progressively becoming essential to the success of research grant procurement and to allow the emergence and evolution of scientific disciplines.Our work focuses on analysing if the volume of collaborations of one author together with the relevance of his collaborators is somewhat related to his research performance over time.To our knowledge, this is the first work that considers success evolution with respect to co-authorship.

View Article: PubMed Central - PubMed

Affiliation: Computer Laboratory, University of Cambridge, Cambridge, United Kingdom; I&C Laboratory, AtlantTIC Research Center, University of Vigo, Vigo, Spain.

ABSTRACT
Collaboration among scholars and institutions is progressively becoming essential to the success of research grant procurement and to allow the emergence and evolution of scientific disciplines. Our work focuses on analysing if the volume of collaborations of one author together with the relevance of his collaborators is somewhat related to his research performance over time. In order to prove this relation we collected the temporal distributions of scholars' publications and citations from the Google Scholar platform and the co-authorship network (of Computer Scientists) underlying the well-known DBLP bibliographic database. By the application of time series clustering, social network analysis and non-parametric statistics, we observe that scholars with similar publications (citations) patterns also tend to have a similar centrality in the co-authorship network. To our knowledge, this is the first work that considers success evolution with respect to co-authorship.

Show MeSH