
Abstract The convergence of multi-disciplinary knowledge may spur emerging technologies. It is important to understand this convergence process that helps to identify these emergent technologies; however, relevant research remains sparse. Therefore, this study aims to develop a novel framework to reveal the convergence process of scientific knowledge. This novel framework integrates the machine-learning topology clustering and visualization methods, and analyzes paper citation networks. This study selects the biological–informatics domain (bioinformatics) to conduct the empirical analysis. This paper finds two major stages throughout the convergence process: the fast-changing incubation stage and the stabilized development stage. In the incubation stage, the interactions between the biology and informatics knowledge domains becomes increasingly intensive, while emergent technology is yet to form; in the stable development stage, the emergent technology starts to form as a core cluster, and based on which it grows amid stabilized knowledge interactions between the original two domains. The revelation of this convergence process contributes to the formation theory of emerging technologies that are inter-disciplinary, and is of great interest to researchers, policy makers, and industrialists.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 72 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 1% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 1% |
