Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Mining potential research synergies from co-authorship graphs using power graph analysis

Mining potential research synergies from co-authorship graphs using power graph analysis Bibliographic databases are a prosperous field for data mining research and social network analysis. They contain rich information, which can be analysed across different dimensions (e.g., author, year, venue, and topic) and can be exploited in multiple ways. The representation and visualisation of bibliographic databases as graphs and the application of data mining techniques can help us uncover interesting knowledge concerning potential synergies between researchers, possible matchings between researchers and venues, candidate reviewers for a paper or even the ideal venue for presenting a research work. In this paper, we propose a novel representation model for bibliographic data, which combines co-authorship and content similarity information, and allows for the formation of scientific networks. Using a graph visualisation tool from the biological domain, we are able to provide comprehensive visualisations that help us uncover hidden relations between authors and suggest potential synergies between researchers or groups. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Web Engineering and Technology Inderscience Publishers

Mining potential research synergies from co-authorship graphs using power graph analysis

Download PDF
23 pages

Loading next page...
 
/lp/inderscience-publishers/mining-potential-research-synergies-from-co-authorship-graphs-using-XY6jLJODxc
Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1476-1289
eISSN
1741-9212
DOI
10.1504/IJWET.2012.048520
Publisher site
See Article on Publisher Site

Abstract

Bibliographic databases are a prosperous field for data mining research and social network analysis. They contain rich information, which can be analysed across different dimensions (e.g., author, year, venue, and topic) and can be exploited in multiple ways. The representation and visualisation of bibliographic databases as graphs and the application of data mining techniques can help us uncover interesting knowledge concerning potential synergies between researchers, possible matchings between researchers and venues, candidate reviewers for a paper or even the ideal venue for presenting a research work. In this paper, we propose a novel representation model for bibliographic data, which combines co-authorship and content similarity information, and allows for the formation of scientific networks. Using a graph visualisation tool from the biological domain, we are able to provide comprehensive visualisations that help us uncover hidden relations between authors and suggest potential synergies between researchers or groups.

Journal

International Journal of Web Engineering and Technology – Inderscience Publishers

Published: Jan 1, 2012

There are no references for this article.