Analyzing evolution of research topics with NEViewer: a new method based on dynamic co-word networks

• DOI: 10.1007/s11192-014-1347-y
• Publication Date: 2014-06-21T13:05:14Z
• Authors: Xiaoguang Wang; Qikai Cheng; Wei Lu
• Keywords: Macro; Software evolution

Understanding the evolution of research topics is crucial to detect emerging trends in science. This paper proposes a new approach and a framework to discover the evolution of topics based on dynamic co-word networks and communities within them. The NEViewer software was developed according to this approach and framework, as compared to the existing studies and science mapping software tools, our work is innovative in three aspects: (a) the design of a longitudinal framework based on the dynamics of co-word communities; (b) it proposes a community labelling algorithm and community evolution verification algorithms; (c) and visualizes the evolution of topics at the macro and micro level respectively using alluvial diagrams and coloring networks. A case study in computer science and a careful assessment was implemented and demonstrating that the new method and the software NEViewer is feasible and effective.