Revisit Girvan-Newman Algorithm for Research Topic Analysis: An Application on Library and Information Science Studies

Abstract

Research trend analysis gives the research community an essential chance to learn the past to support sustainable development. The topic of evolution analysis presents a chance to position the current research, linkages among research topics, and identify the research gap. In this study, the authors revisit a known mechanism, namely Girvan-Newman (GN) algorithm, and propose a new approach for research topic analysis. Based on the GN algorithm, author-keywords analysis approach, one-mode cluster, and duo GN algorithm analysis were suggested and applied to research topic analysis of Library and Information Science studies. The results show that the suggested approach could process major quantity materials and be able to avoid the possible distorted results gained by taking the small size of samples, or two-mode cluster, to ensure the validity of the results. The topics’ hierarchy structure also suggests a different approach that could be used to deconstruct the linkages among research topics for future study.

Keywords

• research topic evolution
• girvan-newman algorithm
• library information science