Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10358376 | Journal of Informetrics | 2014 | 10 Pages |
Abstract
The main objective of this study is to analyze the relationship between research impact and the structural properties of co-author networks. A new bibliographic source, Microsoft Academic Search, is introduced to test its suitability for bibliometric analyses. Citation counts and 500 one-step ego networks were extracted from this engine. Results show that tiny and sparse networks - characterized by a high Betweenness centrality and a high Average path length - achieved more citations per document than dense and compact networks - described by a high Clustering coefficient and a high Average degree. According to disciplinary differences, Mathematics, Social Sciences and Economics & Business are the disciplines with more sparse and tiny networks; while Physics, Engineering and Geosciences are characterized by dense and crowded networks. This suggests that in sparse ego networks, the central author have more control on their collaborators being more selective in their recruitment and concluding that this behaviour has positive implications in the research impact.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science Applications
Authors
José Luis Ortega,