Local bilateral clustering for identifying research topics and groups from bibliographical data

The structure of scientific collaboration networks provides insight on the relationships between people and disciplines. In this paper, we study a bipartite graph connecting authors to publications and extract from it clusters of authors and articles, interpreting the author clusters as research groups and the article clusters as research topics. Visualisations are proposed to ease the interpretation of such clusters in terms of discovering leaders, the activity level, and other semantic aspects. We discuss the process of obtaining and preprocessing the information from scientific publications, the formulation and implementation of the clustering algorithm, and the creation of the visualisations. Experiments on a test data set are presented, using an initial prototype implementation of the proposed modules.