Publications

Abstract : Amid the enormous volume of knowledge generated due to knowledge explosion, to a great extent, scientific literature mining can play a crucial role in research evaluation and tracking of important developments. Linked through citation relations, scientific literature forms network of papers or citation networks. Citation network analysis is gaining recognition as effective tool for research evaluation. Paradigm shift detection is of great importance to a multitude of beneficiaries and prediction of such paradigm shifts is of much greater gravity towards policy making. Recently, a metric named Flow Vergence (FV) gradient is proposed to detect paradigm shift pivots in scientific literature using the property named as FV effect. In this paper, its predictive power is investigated and tested statically and dynamically using publications in the field Nanotechnology for Engineering for a period 1989–2014. Validation included a post analysis validation of the field in 2017. As predictive power of FV gradient is confirmed, it can be regarded as an effective method for prediction of likely paradigm shifts and added to the tool kit of research evaluators and policy makers.