Identifying key research themes is an effective way to chart knowledge structures in a field of research and, in turn, stimulate new ideas and innovation. Most thematic analyses of a research field are based on some form of network analysis, e.g., citations and cowords, and most of these networks are made up of cohesive, highly overlapping groups of nodes. Based on the suggestion that the “universal features” of networks are to be found in these overlapping communities, we argue that these same communities in a keyword network should reveal the key research themes in a field of study. With no traditional method with which to test our theory, we combined a cluster percolation algorithm with a Word2Vec model, and in a case study on information science, we were not only able to detect the overlapping communities in a keyword similarity network, but we also found a new perspective on the importance of overlapping communities as a way to identify a field’s key research themes.
Author(s): Lu Huang, Fangyan Liu, Yi Zhang
Organization(s): Beijing Institute of Technology, University of Technology Sydney
Source: IEEE Transactions on Engineering Management