Patterns of contact and communication in scientific research collaboration
CSCW '88 Proceedings of the 1988 ACM conference on Computer-supported cooperative work
Global and local collaborators: a study of scientific collaboration
Information Processing and Management: an International Journal - Special issue on Informetrics
Connection and stratification in research collaboration: an analysis of the COLLNET network
Information Processing and Management: an International Journal - Special issue: Informetrics
Positional effects on citation and readership in arXiv
Journal of the American Society for Information Science and Technology
Annual Review of Information Science and Technology
Co-authorship networks in the digital library research community
Information Processing and Management: an International Journal - Special issue: Infometrics
Discovering author impact: A PageRank perspective
Information Processing and Management: an International Journal
HICSS '11 Proceedings of the 2011 44th Hawaii International Conference on System Sciences
h-Type hybrid centrality measures for weighted networks
Scientometrics
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In this study, we propose and validate social networks based theoretical model for exploring scholars' collaboration (co-authorship) network properties associated with their citation-based research performance (i.e., g-index). Using structural holes theory, we focus on how a scholar's egocentric network properties of density, efficiency and constraint within the network associate with their scholarly performance. For our analysis, we use publication data of high impact factor journals in the field of ''Information Science & Library Science'' between 2000 and 2009, extracted from Scopus. The resulting database contained 4837 publications reflecting the contributions of 8069 authors. Results from our data analysis suggest that research performance of scholars' is significantly correlated with scholars' ego-network measures. In particular, scholars with more co-authors and those who exhibit higher levels of betweenness centrality (i.e., the extent to which a co-author is between another pair of co-authors) perform better in terms of research (i.e., higher g-index). Furthermore, scholars with efficient collaboration networks who maintain a strong co-authorship relationship with one primary co-author within a group of linked co-authors (i.e., co-authors that have joint publications) perform better than those researchers with many relationships to the same group of linked co-authors.