Dynamic subfield analysis of disciplines: an examination of the trading impact and knowledge diffusion patterns of computer science

Abstract

The objective of this research is to examine the dynamic impact and diffusion patterns at the subfield level. Using a 15-year citation data set, this research reveals the characteristics of the subfields of computer science from the aspects of citation characteristics, citation link characteristics, network characteristics, and their dynamics. Through a set of indicators including incoming citations, number of citing areas, cited/citing ratios, self-citations ratios, PageRank, and betweenness centrality, the study finds that subfields such as Computer Science Applications, Software, Artificial Intelligence, and Information Systems possessed higher scientific trading impact. Moreover, it also finds that Human–Computer Interaction, Computational Theory and Mathematics, and Computer Science Applications are among the subfields of computer science that gained the fastest growth in impact. Additionally, Engineering, Mathematics, and Decision Sciences form important knowledge channels with subfields in computer science.