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

Abstract: Bibliographic databases are a prosperous field for data mining research and social network analysis. They contain rich information, which can be analyzed across different dimensions (e.g., author, year, venue, topic) and can be exploited in multiple ways. The representation and visualization 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 visualization tool from the biological domain, we are able to provide comprehensive visualizations that help us uncover hidden relations between authors and suggest potential synergies between researchers or groups.